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Cognitive patterns in paediatric epilepsy: Intra-individual variability, cognitive patternsand patterns of cognitive change in children with epilepsy on the Wechsler IntelligenceScales for Childrenvan Iterson, L.
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Download date: 02 Nov 2018
Loretta van Iterson
Cognitive Patterns in Paediatric Epilepsy
Intra-individual variability, cognitive patterns and patterns of cognitive change
in children with epilepsy on the Wechsler Intelligence Scales for Children
© Loretta van Iterson, 2015 Proefschrift Neuropsychologie (Engels) Cover photo. Sierras malagueñas, Parque Nacional Sierra de la Tejeda, Almijara y Alhama. Spain, 2012, by Loretta van Iterson. ISBN/EAN 978-90-801507-0-6 NUR: 773 Print Gildeprint, Enschede. No financial support was received for this project.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
Intra-individual variability, cognitive patterns and patterns of cognitive change
in children with epilepsy on the Wechsler Intelligence Scales for Children
ACADEMISCH PROEFSCHRIFT
ter verkrijging van de graad van doctor
aan de Universiteit van Amsterdam
op gezag van de Rector Magnificus
prof. dr. D.C. van den Boom
ten overstaan van een door het College voor Promoties ingestelde commissie,
in het openbaar te verdedigen in de Agnietenkapel
op woensdag 7 oktober 2015, te 14.00 uur
door Loretta van Iterson
geboren te ‘s Gravenhage
Promotiecommissie:
Promotor: Prof. dr. P.F. de Jong Universiteit van Amsterdam
Copromotores: Prof. dr. A.S. Kaufman Yale School of Medicine
Dr. B.H.J. Zijlstra Universiteit van Amsterdam
Overige leden: Prof. dr. H.M. Geurts Universiteit van Amsterdam
Prof. dr. M.W. van der Molen Universiteit van Amsterdam
Prof. dr. W.C.M. Resing Universiteit Leiden
Prof. dr. P. Ghesquière Katholieke Universiteit Leuven
Dr. G. Thoonen Radboud Universiteit Nijmegen
Faculteit der Maatschappij- en Gedragswetenschappen
Contents
Introduction 7 Chapter 2. Intra-individual Subtest Variability on the Dutch Wechsler Intelligence Scales for Children–Revised (WISC-RNL) for children with Learning Disabilities, Psychiatric Disorders, and Epilepsy 23
Chapter 3. Differential effect of lesion side on intra-individual variability in children with focal lateralized epilepsy 39
Chapter 4. Establishing Reliable Cognitive Change in Children with Epilepsy: The Procedures and Results for a Sample with Epilepsy 47
Chapter 5. Duration of epilepsy and cognitive development in children: A longitudinal study 61
Chapter 6. Paediatric epilepsy and comorbid reading, math and autism spectrum disorders: impact of epilepsy on the cognitive patterns 81 Discussion 109
Summary (English summary) 133
Samenvatting (Dutch summary) 143
Resumen (Spanish summary) 153
Appendices 161
Appendix A Subtest scaled score range (subtest scatter) 166
Table A.1. Mixed referred and non-referred samples. Characteristics of
the samples. 167
Table A.2. Base rate table. Subtest scatter on 5 subtests of the verbal scale
and on 5 subtests of the performance scale. 168
Table A.3. Base rate table. Subtest scatter on 10 subtests of the full scale. 169
Appendix B. Base rate tables: Verbal – Performance Discrepancies 170
Table B.1. Characteristics of the samples. 171
Table B.2. Base rate tables. VIQ > PIQ. 172
Table B.3. Base rate tables. VIQ < PIQ. 173
Appendix C. Base rate tables for the discrepancies between factor index
scores.Verbal Comprehension (VCI), Perceptual Organization (POI) and
Processing Speed (PSI): (VCI – POI , VCI – PSI, POI – PSI). 174
Table C.1: Characteristics of the samples. 175
Table C.2. Base rate tables. VCI – POI discrepancy. 176
Table C.3. Base rate tables. VCI – PSI discrepancy. 177
Table C.4. Base rate tables. POI – PSI discrepancy. 178
Appendix D. From Test 1 to Test 2. Cognitive Change After Serial Testing. 179
Table D.1. Characteristics of the samples. 180
Table D.2. Base rate table. Cognitive gains and cognitive losses on the
verbal scale. 181
Table D.3. Base rate table. Cognitive gains and cognitive losses on the
performance scale. 182
Table D.4. Base rate table. Cognitive gains and cognitive losses on the
full scale. 183
Table D.5. Base rate table. Cognitive Change on the Factor Index scores.
Characteristics of the sample. 184
Table D.6. Base rate table. Cognitive gains and losses on VCI, POI, and PSI. 185
Appendix E. Isolated epilepsy, isolated developmental disorders and
comorbidities in epilepsy: ROC images for Chapter 6. 186
Table E. Rate of children showing VIQ – PIQ discrepancies of 15 or more
points. 188
References 189
Additional publications and posters (related and unrelated topics) 202
Acknowledgements 205
About the author 211
Introduction
INTRODUCTION
9
Introduction
There is increasing evidence that when epilepsy occurs in childhood, it will have a major
impact on the course of the child’s life in terms of cognition, learning, behaviour and,
ultimately, psychosocial outcome (Camfield & Camfield, 2007; Hermann, Jones, Jackson,
& Seidenberg, 2012). Cognitive problems have been found to be one of the major factors
accounting for psychosocial outcome (Camfield & Camfield, 2007). Cognitive
development in children with epilepsy will be the topic of the present work.
Epilepsy is a heterogeneous disorder and shows diversity in terms of age at onset,
presentation, severity, response to treatment, duration, accompanying comorbidities, and
cognitive course. Given this diversity, it is not surprising that when it strikes, in terms of
cognitive outcomes, epilepsy will be considered a relatively benign disorder in some
children, and a relatively complicated disorder in other children. The present work will
relate to children with “not uncomplicated” epilepsy, that is, children referred to a tertiary
epilepsy centre or its affiliated epilepsy school for neuropsychological assessment
because of concerns about their cognitive development. The principal topics of interest
will be intra-individual variability within test scores, patterns of cognitive abilities in
children tested for the first time, and patterns of change over time of children who have
been tested more than once. The studies will compare the patterns with those of children
with other developmental disorders as well as with those of children with double
diagnoses of epilepsy and a learning or behavioural disorder, that is, of children with a
comorbid disorder.
To better understand the scope of the field of epilepsy and cognition, the following
pages will start with a definition of epilepsy and its occurrence in children. The main
topic, cognitive patterns displayed by the verbal and performance (nonverbal) scales of
the Wechsler Intelligence Scales for Children (WISC series) will be discussed in
association with epilepsy variables known to affect cognition in epilepsy: age at onset,
duration, severity, brain lesions and comorbidities. At the end of the chapter, attention
will turn to the research questions investigated in the present work.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
10
Epilepsy in Children
Definition, Prevalence, Incidence and Classification. The International League
Against Epilepsy (ILAE) defines epilepsy as a neurological disorder characterized by an
“enduring disposition of the brain to generate epileptic seizures” (Fisher et al., 2005, p
470) and epileptic seizures as “transient occurring signs or symptoms due to abnormal
excessive or synchronous neuronal activity in the brain” (Fisher et al., 2005, p 470). That
is, clinically, epilepsy is characterized by seizures, and its electrophysiological
counterpart is abnormal electrical activity seen on the EEG.
Epilepsy may have its onset at any age, but children are particularly affected. The
proportion of children affected by epilepsy (prevalence) is almost one percent (Russ,
Larson, & Halfon, 2012; Sillanpää, 1992). The probability of occurrence of epilepsy
(incidence) is ~64 per 100.000 children per year of age. Incidence is highest in the first
year of life, with ~102 per 100.000 children. In the second to fourth year, incidence is ~65
per year; incidence declines to ~25 for the teenage years (Wirrell, Grossardt, Wong-
Kisiel, & Nickels, 2011).
The classification of seizures, epilepsies and epilepsy syndromes (I.L.A.E., 1981,
1989) has formed the basis of diagnosis, treatment and research on epilepsy during the
past decades. The classification system has been further revised and updated (Berg &
Scheffer, 2011; Engel, 2006) The classification system for both seizures and epilepsies,
used over the years and largely maintained in the newest classification, is based on
various levels of classification:
One level concerns seizure type. Focal seizures, also called partial seizures or
localization-related seizures, are limited to specific areas of the brain (like frontal lobe
seizures) and are mostly limited to a hemispheric side of seizure onset (right versus left
hemisphere). Further distinctions in the description of focal seizures relate to the
lateralization (left versus right hemisphere onset) and to the topographical localization
(frontal, temporal, parietal, central, occipital and combinations of these). Generalized
seizures involve both hemispheres. They may start at one hemisphere and instantly spread
to the other side.
A second level is aetiology. In idiopathic epilepsy no underlying cause other than
a possible genetic predisposition has been found. In symptomatic epilepsy, an underlying
cause, as an MRI-abnormality or another known aetiology is identified. A presumed, but
not identified cause, “probably symptomatic”, has been called cryptogenic, while the term
unknown is now being suggested for this group of seizures. Furthermore, there are
INTRODUCTION
11
epilepsies and epileptic syndromes where it is undetermined or uncertain whether the
seizures are focal or generalized, like the epileptic encephalopathies. Generally,
idiopathic epilepsies are associated with better cognitive outcomes than those of unknown
origin or symptomatic epilepsies (Nolan et al., 2003). For the term idiopathic epilepsy,
the notion of genetic causation is being used. For epilepsies with identified genetic
causes, however, cognitive outcome may show large variation (Olson, Poduri, & Pearl,
2014).
The Impact of Epilepsy on Cognition
One of the major areas of concern in paediatric epilepsy is cognitive development.
Children with epilepsy score an average 11 points lower on IQ tests than children without
seizures and about four points lower than their siblings without seizures (Ellenberg, Hirtz,
& Nelson, 1986). Half of the children have developmental delays, in contrast to 3% of the
general population (Russ et al., 2012); about 26% present with IQs below 80 (Berg et al.,
2008a), whereas IQ scores that low would be expected in only 9.2% of the general
population. Neurobehavioral comorbidities like learning disorders and behavioural
disorders have been frequently reported as well (Russ et al., 2012), and are becoming an
area of increased interest (Lin, Mula, & Hermann, 2012).
Verbal and nonverbal abilities. Verbal and nonverbal – hence performance –
abilities have historically been the core cognitive abilities within the broad domain of
intellectual functioning. Myriad studies have shown that verbal and performance abilities
are compromised in epilepsy (Aldenkamp, Alpherts, De Bruine-Seeder, & Dekker, 1990;
Bjornaes, Stabell, Henriksen, & Loyning, 2001; Braakman et al., 2012; Gülgönen,
Demirbilek, Korkmaz, Dervent, & Townes, 2000; Lopes et al., 2013; Northcott et al.,
2007; O'Leary, Burns, & Borden, 2006; Overvliet et al., 2011; Vermeulen, Kortstee,
Alpherts, & Aldenkamp, 1994).
Verbal and performance abilities are sampled in a standardized manner in the core
scales of the WISC series and encompass a wide range of cognitive skills (van Haasen et
al., 1986; Wechsler, 1992, 2004, 2005). The present work will focus on these core
abilities, the verbal and performance abilities, in children with epilepsy and will use the
Dutch Wechsler Intelligence Scales to measure them. The newest Wechsler test in The
Netherlands is the WISC-III (Wechsler, 2005), which, like the WISC-R (van Haasen et al.,
1986) still adheres to the traditional dichotomy of verbal and performance scales. The
verbal IQ scales (VIQ) and verbal comprehension indexes (VCI) are broad measures of
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
12
verbal abilities and include various subtests, which assess understanding of questions,
general knowledge, verbal reasoning and abstraction, and knowledge of the meaning of
words. The performance IQ scale (PIQ) and the perceptual organization or perceptual
reasoning index (POI/PRI) also are broad measures of nonverbal abilities, and include
several subtests which assess visual spatial reasoning and depend also on constructional
abilities, motor dexterity and speed. The scales measuring verbal and performance
abilities have been part of the Wechsler intelligence scales for over 70 years. They
remained largely unchanged for decades in the early WISC versions (up to WISC-III) but
they have been modified in the latest versions (WISC-IV, and the WISC-V, which was
published in the US in 2014) to rely less on motor skills and speed (Baron, 2005;
Flanagan & Kaufman, 2009). The newest versions of the Wechsler tests show a growing
tendency towards including more pure measures of neuropsychological functions (like
speed of processing and working memory) as factor indexes. However, even now that
current versions emphasize separate Indexes rather than separate IQs, the verbal and
nonverbal indexes (Verbal Comprehension Index and Perceptual Reasoning Index in 4th
editions) have a special status relative to other Indexes; they alone are combined to
constitute the General Ability Index, an alternate to Full-Scale IQ (FS-IQ, Flanagan &
Kaufman, 2009; Wechsler, 2004; Weiss, Saklofske, Schwartz, Prifitera, & Courville,
2006). The intelligence scales, and particularly the verbal scales, of both the original and
the newer versions, are found to be associated with learning and school achievement
(Glutting, Watkins, Konold, & McDermott, 2006; van Haasen et al., 1986).
Verbal and performance abilities, as measured by the intelligence scales for
children are frequently reported in studies on epilepsy, either as a topic of interest in the
study or as standard background information on the samples. Although Wechsler scales
are widely used, published studies on the WISC are not always readily comparable. This
is partly due to the changes in the newer test versions, but also because some researchers
have not used all 10 verbal and performance subtests (or 13, to include the indexes), but
rather have administered short forms. These short forms have ranged from one subtest
only (Oostrom, van Teeseling, Smeets-Schouten, Peters, & Jennekens-Schinkel, 2005) to
eight subtests (Bailet & Turk, 2000; Berg et al., 2008a, 2008b; Gülgönen et al., 2000).
Existing epilepsy studies usually contrast children´s scores on the verbal and
nonverbal scales to a reference sample like a sample of healthy control children
(Braakman et al., 2012; Gülgönen et al., 2000; Northcott et al., 2007; O'Leary et al.,
2006). Alternatively, the scores on a scale on one sample with epilepsy may be contrasted
INTRODUCTION
13
to another sample with epilepsy, as left versus right hemisphere onset seizures, or frontal
versus temporal epilepsy (Campiglia et al., 2014; Lopes et al., 2013; Miranda & Smith,
2001). Studies on variability in test profiles within an individual, that is, studies on
cognitive patterns, however, are relatively scarce.
Variability in test profiles. Some authors have suggested that there is a need for
more fine-grained studies on cognitive development in epilepsy (Hermann et al., 2012).
One way of making finer distinctions is studying cognitive profiles or cognitive patterns.
Cognitive patterns focus on relative strengths and weaknesses in a cognitive profile and
are therefore measures of variability within a test profile. While data on verbal and
performance scales are often reported in studies in epilepsy in children, relatively fewer
studies have been done on the cognitive patterns, and not much is known about the
epilepsy variables which may influence such patterns.
One of the measures of variability is the intra-individual subtest variability or
subtest “scatter”. Subtest scatter relates to the difference between the highest and the
lowest subscale within the verbal, the performance or the full scale. Although studies on
adults with normal intelligence have associated elevated scatter with brain lesions (Ryan,
Tree, Morris, & Gontkovsky, 2006), intra-individual subtest variability has not yet
received attention in the literature on epilepsy. This may be because the importance of
intra-individual subtest variability has been the subject of debate. Some researchers
advocate against the use of intra-individual subtest variability measures (Watkins &
Glutting, 2000; Watkins, Glutting, & Youngstrom, 2005), while at the same time, the
information is becoming standard in the test manuals or “companions” to test manuals of
English speaking countries (Kaufman, 1979; Wechsler, 2004) in order to be used by
clinicians.
It is still unknown, whether there is increased intra-individual subtest variability in
children with epilepsy, and how this relates to scatter in other clinical samples. In
Chapters 2 and 3, intra-individual subtest variability will be studied in large samples of
referred children, both with and without epilepsy. The aim is to investigate whether
relatively large amounts of scatter can be interpreted as a characteristic of clinically
referred samples, and, as such, as some kind of “pathology”. Also, which scale, if any, is
particularly prone to show elevated scatter in a particular neurodevelopmental disorder. In
addition, intra-individual subtest variability will be studied in relation to epilepsy
variables, particularly lateralization of epilepsy and presence of brain lesions on
neuroimaging.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
14
A second measure of variability within a test profile is the difference displayed by
a child on the verbal and performance scales, the VIQ – PIQ discrepancy. The
discrepancy between the two scales provides information about whether one of the scales
is more vulnerable to seizures than the other. This is important because remediation can
be targeted to address the weaknesses in the profile, either directly, or indirectly by taking
advantage of the strengths to compensate for the weaknesses. It has been understood that,
in children, the left and right hemisphere functions are not mirrored in the verbal and
performance scales of the Wechsler scales (Miranda & Smith, 2001). Also, it has been
found that after epilepsy surgery, the performance abilities show a slightly better recovery
than the verbal abilities, regardless of side of surgery (Westerveld et al., 2000). In
addition, some evidence has been presented for a deleterious impact of daytime seizures
especially on the performance scale, and for night time seizure activity especially on the
verbal scale (Overvliet et al., 2011). Overall, however, the pattern displayed by the verbal
and performance scale has seldom been the direct focus of studies. It is possible,
however, to assess the pattern indirectly through the WISC scores provided as descriptive
background information. As will be discussed in one of the next chapters (Chapter 6),
when assessed indirectly, the literature remains inconclusive as to whether epilepsy shows
its impact on one of the scales (VIQ or PIQ) differentially, and which variables are
associated with lowered verbal or performance abilities. This question, the differential
impact of epilepsy (and epilepsy variables) on verbal and performance abilities, will be
the main topic in the present work.
This differential impact can be studied in association with epilepsy variables, like
lateralization, seizure type, or medication, but it is also important to relate the possible
differential impact to time-related aspects, like the onset of the seizure condition early or
later in life, and a shorter or longer duration of the seizure condition. It should be
explored, for example, whether cognitive patterns change over time with increased
duration of the seizure condition. In addition to the epilepsy variables, the differential
impact of epilepsy on cognition could also be explored in the context of other
neurodevelopmental disorders in the child, that is, in children who have behavioural or
learning disorders in addition to epilepsy. These two topics – time-related aspects in
epilepsy and comorbidities in epilepsy – will be discussed in the following paragraphs.
Cognitive change over time. Epilepsy may often be considered a long term, and
sometimes even life-long condition, with remission and relapses (Geerts et al., 2010;
Koepp, Thomas, Wandschneider, Berkovic, & Schmidt, 2014; Schmidt & Sillanpää,
INTRODUCTION
15
2012). A Dutch 15-year follow-up study on childhood onset epilepsy using questionnaires
after two, five and fifteen years, indicated that 49% of the children were seizure free
within two years and remained in remission at all measurement points. An additional 29%
became seizure free after 2-5 years. As many as 12% showed a varying course of
remissions followed by relapses and 10% were never seizure free longer than three
months (Geerts et al., 2010). Mean duration of the seizure condition was six years,
ranging from 0 to 21 years (Geerts et al., 2010). Similar results were reported by Schmidt
and Sillanpää (2012). Relapse of seizures may occur even after a seizure-free period as
long as seven years (Berg, Testa, & Levy, 2011). Anti-epileptic drugs (AEDs) are the
treatment of choice for epilepsy and although the majority of children achieve seizure
freedom with medication, a considerable number of children are difficult to treat and
continue to have seizures for a prolonged period of time. This raises the question of the
long-term impact of epilepsy on the cognitive development of a child.
Variables like age at onset and aetiology of the epilepsy have been associated with
increased severity of the epilepsy and worse cognitive outcome, although for none of
these variables results can be considered conclusive.
An onset of epilepsy (AOE) early in life will show worse cognitive outcome than
epilepsy which starts later in childhood (Berg et al., 2008a; Berg, Zelko, Levy, & Testa,
2012; Cormack et al., 2007). Berg et al. (2012) pointed out, however, that the bad
outcomes in early-life epilepsy are mediated by the response to anti-epileptic medication:
87% of the infants who developed epilepsy in the first year of life and did not respond
favourably to medication showed an IQ below 80 when reassessed eight years later. In
contrast, in a sample of children with older age of onset (7 years), none had an IQ below
80 later on, even if the children showed resistance to antiepileptic medication.
Epilepsy syndromes with known aetiology are associated with poor cognitive
outcome. Scales on syndrome severity (Dunn, Buelow, Austin, Shinnar, & Perkins, 2004)
rate these epilepsies as most severe or “most complicated”; fortunately they are relatively
infrequent epilepsies (Covanis, 2012). The most complicated epilepsies, like the epileptic
encephalopathies are, by definition, associated with cognitive arrest and cognitive
deterioration (Covanis, 2012; van Rijckevorsel, 2006). Underlying brain pathology (Berg
et al., 2012), generalized symptomatic epilepsies (Nolan et al., 2003) and failure to
respond to medication have been associated with worse outcome (Berg et al., 2012).
However, lowered IQ can be associated with any kind of epilepsy. Lowered IQ has been
found in children with epilepsies of “moderate severity”, like temporal lobe epilepsy
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
16
(Miranda & Smith, 2001; Westerveld et al., 2000), frontal lobe seizures (Braakman et al.,
2012), and cryptogenic focal epilepsy (Van Mil et al., 2010). In addition, epilepsies which
have been considered of “low severity” in a medical sense for a long time may still be
accompanied by cognitive problems. Such is the case for benign epilepsy with centro
temporal spikes (BECTS) which is often accompanied by language and reading problems
(T. Clarke et al., 2007; Northcott et al., 2007; Overvliet et al., 2011). Childhood absence
epilepsy (CAE), also considered a relatively mild type of epilepsy, is frequently
associated with impaired attention, even if there is satisfactory response to medication
(Masur et al., 2013).
After reviewing longitudinal studies on cognitive change in epilepsy, various authors
conclude that cognitive change in children with epilepsy manifests itself as cognitive
decline, sometimes referred to as “cognitive progression” (Dodrill, 2004; Seidenberg,
Pulsipher, & Hermann, 2007). The same authors conclude that cognitive progression and
its relationship to epilepsy variables is still insufficiently understood and that more studies
are needed (Dodrill, 2004; Seidenberg et al., 2007). More studies, including several
longitudinal studies, have appeared later on, but they have paid insufficient attention to
the broad spectrum of epilepsies. Rather, various studies focussed on relatively
“uncomplicated” epilepsies. While there is no formal definition of “uncomplicated
epilepsies”, the term will be used to designate non-referred children, children with a short
duration of epilepsy and children with an onset of epilepsy later in childhood. Studies on
children with uncomplicated epilepsies have reported a normal cognitive level (Berg,
Hesdorffer, & Zelko, 2011; Piccinelli et al., 2010), as well as a normal development over
time with only minor differences from healthy controls (Ellenberg et al., 1986; Hermann
et al., 2008; Jones, Siddarth, Gurbani, Shields, & Caplan, 2010). In a follow-up study on
cognitive outcome after the epilepsy had remitted, residual cognitive effects limited to
difficulties in processing speed were found (Berg et al., 2008b). Again, the study was
based on uncomplicated epilepsy, with a short duration of seizures (5 years of seizure
freedom at 8-year follow-up). The children were largely off medication and they were
selected for having FS-IQs above 80 at the last measurement and coming from families
where the siblings also had normal IQs (Berg et al., 2008b). A relatively uncomplicated
cognitive course, with no large differences from controls (Hermann et al., 2008; Oostrom
et al., 2005), has also been reported in longitudinal studies that selected children with an
onset of epilepsy relatively late in childhood (with a mean age at onset of almost nine
INTRODUCTION
17
years, as in Oostrom et al.), or in early adolescence (almost 12 years as in Hermann et al.,
2008). These studies on uncomplicated epilepsies are of great value because they aim at
isolating the impact of seizures themselves on cognitive development in children. The
results are encouraging and indicate that children’s development shows resilience in the
light of a single adverse event, epileptic seizures.
Many seizure conditions, and especially those of children attending specialized
centres, are not “uncomplicated”. Children with “not uncomplicated” epilepsies are
referred to psychological evaluation or special services because concerns have risen; they
often have not shown satisfactory response to the first medication and continued to have
seizures over time.
Whereas complicated epilepsy appears as a clinically more urgent topic of study,
and inclusion of a wide spectrum of epilepsies is encouraged (Nolan et al., 2003), much
has still to be unravelled when it comes to understanding cognitive change over time in
referred children (Hermann et al., 2012).
The present work will study changes of cognitive abilities – cognitive decline –
over time in children who were followed longitudinally. Again, the focus will be the
pattern of verbal and performance abilities and the changes that may occur during the
course of the epilepsy differentially on the verbal and performance abilities. Cognitive
decline of the verbal and performance abilities will be studied longitudinally in relation to
epilepsy variables thought to be associated with more complicated epilepsies like early
age at onset and longer duration of epilepsy. In addition, rates of children showing
reliable cognitive change will be established to know what proportion of children is likely
to show a clinically meaningful change in IQ. Reliable cognitive change refers to
cognitive changes seen between serial measurement points which surpass a specified cut-
off value and are therefore considered clinically meaningful changes (Chelune, Naugle,
Lüders, Sedlak, & Awad, 1993).
Comorbidities. Strictly speaking, when two conditions co-occur in an individual,
they are understood as being comorbid conditions (Angold, Costello, & Erkanli, 1999).
Alternatively, a narrower and more common definition proposes that when epilepsy and
other developmental disorders co-occur at rates higher than expected, these
developmental disorders are referred to as comorbidities in epilepsy (Brooks-Kayal et al.,
2013; Lin et al., 2012). Comorbidities add to the burden of epilepsy (Berg, Caplan, &
Hesdorffer, 2011) and impact on the quality of life. Therefore, it is not surprising that the
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
18
plea to study comorbidities in epilepsy is sounding increasingly louder (Asato, Caplan, &
Hermann, 2014; Helmstaedter et al., 2014).
Psychiatric and behavioural comorbidities. Epilepsy in children is associated with
an increased risk of psychiatric and behavioural comorbidities. Comorbidities described
in epilepsy are attention deficit disorders, conduct disorders, anxiety, and depression
(Hermann et al., 2008; C. J. Reilly, 2011; Russ et al., 2012). In addition, one of the
comorbidities of major concern in epilepsy is autism spectrum disorders (ASD). Rates of
ASD in epilepsy range from 15 to 32% (Berg & Plioplys, 2012; D. F. Clarke et al., 2005;
Russ et al., 2012). ASD comorbid with epilepsy is considered a major challenge in terms
of treatment (Tuchman, Alessandri, & Cuccaro, 2010), and ASD and epilepsy are often
seen in combination with cognitive impairment (Berg & Plioplys, 2012).
School achievement difficulties. School problems are common in children with
epilepsy, including children with epilepsy who have IQs in the average range (Austin,
Huberty, Huster, & Dunn, 1999; C. Reilly & Neville, 2011). Russ et al. (2012) presented
epidemiological data showing that up to 75% of children with epilepsy needed special
(individualized) educational services, 52% of children with seizures had learning
problems, and 32% repeated a grade (Russ et al., 2012). Fastenau et al. (2008) found
academic underachievement (learning problems discrepant with IQ) in ~50% of the
children. About 40 to 60% of the children showed low achievement in at least one area
(reading, arithmetic, writing to dictation and writing samples), wherein writing was the
most frequently affected area.
Achievement difficulties have been reported to antedate the emergence of the
epileptic condition proper, as reflected in grade repetition and use of special services in as
many as ~23% of the children who later developed epilepsy (Berg, Hesdorffer, et al.,
2011; Schouten, Oostrom, Jennekens-Schinkel, & Peters, 2001). Therefore, learning may
be understood as a cognitive domain which is vulnerable to the epilepsy which is about to
emerge, but learning disorders may also be comorbidities which manifest themselves
already before the onset of epilepsy.
Different models have been described to understand the co-occurrence of epilepsy
and comorbidities. Pal (2011), for example, suggests that in some cases the comorbidity
can be understood as being caused by the seizure condition, while in other cases the
epilepsy and the comorbidity appear as distinct conditions, with shared or independent
causal factors. In addition, Pal suggests that in yet other cases, the presence of epilepsy
INTRODUCTION
19
may modify the comorbid disorder, for example worsening it (to illustrate, a language
disorder may appear worse if accompanied by seizures).
Thus, while it is known that behavioural and learning disorders are frequent
comorbidities in epilepsy, a question which remains largely open is whether these
comorbidities present in a similar fashion, that is, with similar cognitive patterns, as in
their isolated condition without epilepsy. Conversely, not much is known on the impact
the comorbidity exerts on the cognitive pattern of the child with epilepsy. If epilepsy and
comorbidity would influence cognitive patterns, this could have clinical implications in
terms of diagnosis and remediation. These questions will be addressed in in Chapter 6.
Present Study
While cognitive development is a well-researched topic in epilepsy in children,
there have been relatively fewer studies on cognitive patterns in epilepsy. It is still largely
unknown whether there is increased intra-individual subtest variability in epilepsy and
whether the verbal and performance abilities are affected differentially. If so, what
epilepsy variables are related to such patterns? Also, whereas epilepsy is a long-term
condition, it remains largely unknown whether patterns remain stable over time or show
change over time. While comorbidities are frequent in epilepsy, little is known about the
impact of epileptic comorbidities on cognitive patterns. These will be the topics of the
present work.
The studies are based on large numbers of referred children who have been
surveyed in a tertiary epilepsy clinic or who have been receiving special educational
services over a prolonged period of time. The children were “selected” children in the
sense that they were all referred to tertiary settings for clinical neuropsychological
evaluation or educational services. They can be considered as “unselected” in the sense
that they encompassed a wide range of epilepsies in terms of age of onset, type and
severity of epilepsy, duration of seizures, aetiology and cognitive level.
In the end, research in neuropsychology should be of utility for the clinical setting.
It should be “consumer friendly” (Chelune, 2002). Where relevant, the different chapters
will aim at providing cut-off values and data on classificatory statistics. As suggested by
various authors (Watkins et al., 2005; Woods, Weinborn, & Lovejoy, 2003) data on
sensitivity and specificity, or Receiver Operating Characteristics (ROC) analysis, will be
added.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
20
Participants. The present study will focus on children between the in ages 4 to 16
years with a diagnosis of epilepsy who were clinically referred to a tertiary epilepsy
centre, or to a school providing services for children with epilepsy associated with the
centre. They were referred for special school services within a normal or special school
setting – with a partial overlap between the children known to the epilepsy centre and
those known to the school providing special education services. The data were
observational and were collected over a protracted period of time (2002 – 2014), but
archival data were searched as well on children tested earlier.
The children can be considered representative for children with epilepsy referred
for psychological assessment in The Netherlands, and as such, for children with “not
uncomplicated” epilepsy. In this sense, they are likely to belong to the 75% of the
children found in an epidemiological study (Russ et al., 2012) who need some kind of
specialized assistance in school. In the Netherlands, there are two epilepsy centres with
their corresponding school services which provide tertiary epilepsy care nationwide: one
centre and school for the northern half of the country, and one centre and school for the
south. The two centres may be considered largely equivalent. The two schools work
together in a nationwide school service centre on education and epilepsy. These school
services provided for by the nationwide centre are not restricted to the children known to
the tertiary epilepsy clinic or the pupils from the epilepsy school. Rather, they are offered
to all children with epilepsy, regardless of their school setting, provided that an
independent committee has entitled them for these services (Pijl & Pijl, 1998; Resing et
al., 2002). The children of the present studies came from the center in the north at its
associated school.
Research questions.
The main research questions addressed will be:
(1) Is intra-individual subtest variability elevated in epilepsy? It will be studied
whether increased intra-individual subtest variability is elevated in clinical
samples with and without epilepsy (Chapter 2); and whether intra-individual
subtest variability in epilepsy is associated with seizure variables (seizure
lateralization and presence of lesions on neuroimaging) (Chapter 3). The
hypothesis tested is that children with developmental disorders show elevated
intra-individual subtest variability; that is, that elevated intra-individual subtest
variability can be seen as a sign of pathology.
INTRODUCTION
21
(2) Do children with epilepsy have elevated rates of Reliable Cognitive Change? The
hypothesis tested in Chapter 4 will be that children with epilepsy are more liable
to show reliable cognitive change than other referred children, and that this
change presents predominantly as cognitive decline.
(3) What is the pattern of cognitive change seen in longitudinal studies in epilepsy
over time? In children tested two or three times, time-related variables like age at
onset and duration of epilepsy, and other variables associated with severity of
epilepsy, will be tested in relation to the change (i.e., decline) which can be seen
on the verbal and performance scales (Chapter 5). The hypothesis will be that in a
heterogeneous sample of relatively complicated epilepsies, cognitive decline can
be seen and that this decline affects the verbal and performance scales differently.
Based on the literature, it is hypothesized that epilepsy variables suggestive of
higher severity are likely to be associated with greater cognitive decline over time.
The study will also take into account participation in special education and lower
parental education; both variables are expected to be associated with lower IQs.
(4) What is the impact of comorbidities in epilepsy on cognitive patterns? Based on
children with epilepsy, with and without comorbid developmental disorders, and
on children without epilepsy but with other developmental disorders, it will be
studied whether the patterns from “isolated” conditions (a single diagnosis) differ
from those with comorbid conditions (a double diagnosis of epilepsy and another
disorder). The developmental disorders studied will be learning disorders (reading
disorders, math disorders) and of autism spectrum disorders (Chapter 6). The
hypothesis tested will be that the co-occurrence of epilepsy and a developmental
disorder will affect the cognitive pattern, leading to patterns different from those
of isolated conditions.
CHAPTER 2
Intra-individual Subtest Variability on the Dutch Wechsler Intelligence
Scales for Children–Revised (WISC-RNL) for children with Learning
Disabilities, Psychiatric Disorders, and Epilepsy
Loretta van Iterson
Alan S. Kaufman
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
24
Abstract
It is common practice to look at disparities among subtest scores (“scatter”) on an
intelligence test to establish if a score is deviant. However, it remains unclear whether
subtest scatter reflects primarily normal variation within individuals or is clinically
meaningful. The present study explored this issue based on data from 467 children with
developmental disabilities tested on the Dutch WISC-RNL. Of these children, 132 had
learning disabilities, 178 had psychiatric disorders, and 157 had epilepsy. Subtest scatter
was defined as scaled-score range (highest minus lowest scaled score). When contrasted
with “normal scatter,” the overall sample revealed higher ranges on the performance scale
and full scale, although effect sizes were small. Analysis of the data for the three separate
clinical samples revealed unusual scatter only for the sample of children with psychiatric
disorders. When comparing the clinical samples, scaled-score range was larger for the
sample of children with psychiatric disorders than for those with epilepsy. Two distinct
subsamples revealed elevated ranges with moderate effect sizes: children with autistic
spectrum disorders and children with left hemisphere seizures. These results suggest that
elevated subtest scaled-score range might characterize specific clinical samples rather
than denoting an overall sign of pathology.
van Iterson, L., & Kaufman, A. S. (2009). Intra-individual subtest variability on the Dutch Wechsler Intelligence Scales for Children-Revised (WISC-RNL) for children with learning disabilities, psychiatric disorders, and epilepsy. Psychological Reports, 105(3 Pt 2), 995-1008.
INTRA-INDIVIDUAL SUBTEST VARIABILITY
25
In child neuropsychology, the clinician frequently looks for strengths and weaknesses in
the cognitive profile, often operationalized as a positive or negative difference of 1 or 2
standard deviations, in order to make a diagnosis of a developmental disorder (Sattler,
2001). This approach is based on the assumption that a child’s profile should be uniform,
and that undue inter-subtest or intra-individual variability (scatter) can be interpreted as
pointing toward a specific strength or deficit. Two common indexes of scatter are subtest
scaled-score range (the simple difference between the highest and the lowest score in a
profile) and univariate scatter (the number of subtests deviating 1 SD from an individual’s
own mean). Kaufman (1976, 1979) showed that large intra-individual variability on these
indexes, far from being unusual, was seen frequently in the standardization sample of the
WISC-R. Later, Silverstein (1987) demonstrated that the empirically-derived moments
(mean and SD) from Kaufman's data were a function of the psychometric qualities of the
test and could be estimated from the average intercorrelations among the subtests
comprising the scales. Both subtest scaled-score range and univariate scatter make use
only of the extreme values in a profile. As a more sensitive measure of intra-individual
variability, the Profile Variability Index was proposed which, like a standard deviation,
uses information derived from all subtests (Matarazzo, Daniel, Prifitera, & Herman, 1988;
McLean, Reynolds, & Kaufman, 1990). Interestingly, subtest scaled-score range was
shown to correlate highly with Profile Variability Index (Boone, 1993; Matarazzo et al.,
1988).
The question of whether elevated intra-individual variability is a sign of
pathology, or only a reflection of the psychometric properties of the test, remains
unsettled. Some researchers have provided evidence for elevated variability in pathology
(Mayes, Calhoun, & Crowell, 1998; Ryan, Tree, Morris, & Gontkovsky, 2006; Zimet,
Goodman Zimet, Farley, Shapiro Adler, & Zimmerman, 1994), while others ardently
advocate against any use of measures based on inter-subtest variability (Watkins &
Glutting, 2000; Watkins, Glutting, & Youngstrom, 2005). In the studies by Watkins and
his colleagues, inter-subtest variability did not have a significant incremental validity in
predicting academic achievement over and above full-scale IQ in samples of exceptional
children, mainly children with learning disabilities. The authors argue that inter-subtest
variability is of no use as a diagnostic indicator, and its use can be considered
“prescientific” (Watkins et al., 2005, p.263).
In spite of this controversy, recent Wechsler test manuals have incorporated
subtest scaled-score range in the form of base rate tables (e.g., for the WISC-III,
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
26
Wechsler, 1992; and for the WISC-IV, Wechsler, 2004b). For the Dutch, the adult test
version includes subtest scaled-score range (Wechsler, 2004a), while the children’s
versions do not (van Haasen et al., 1986; Wechsler, 2005).
In order to be a sign of pathology, the intra-individual variability should be
significantly different in clinical samples when compared to the standardization sample.
Significant scatter should not only be interpreted as reliable scatter – that is, genuine and
not the effect of measurement error – but also as uncommon, in the sense that the
magnitude of occurrence within the normal population is small, e.g., 5% (Crawford &
Allan, 1996).
Because the Wechsler scales are well-standardized and well-researched, they are
used, in the present paper, as a model to further evaluate whether intra-individual
variability is a clinically meaningful measure of pathology. This study focused on subtest
scaled-score range because it is practical and easily computed by clinicians and is
included in most recent test manuals. Furthermore, it correlates highly with Profile
Variability Index. Univariate scatter, on the other hand, was not included because its
distribution is skewed, preventing parametrical analyses. The data refer to the Dutch
WISC-R (for this purpose, WISC-RNL; van Haasen et al., 1986) and were collected up to
2007. This version was in use in the Netherlands for a prolonged time, thus allowing the
collection of larger samples. For the newer test version, it will take some time before
sufficiently large samples are accrued, but underlying notions about subtest scatter can be
understood independent of test version. The expected mean values of subtest scaled-score
range and the cut-off values for uncommonly large ranges for the WISC-RNL were
estimated according to Silverstein (1987; 1989), aided by Owen’s (1962) range statistics.
These estimates draw on the averaged intercorrelations between the subtests, which came
from the technical manual of the WISC-RNL (de Bruyn, Vandersteene, & van Haasen, 1986,
p. 139, from N = 1961 children).
Based on WISC-RNL data on 467 children from three clinical samples, the aims of
this study were (1) to study whether subtest scaled-score range in children with
developmental disabilities shows differences compared to expected (normal) values; (2)
to study whether there are differences among the clinical samples, and, if so, (3) to
explore whether specific subsamples account for these differences; and (4), to report rates
of individuals with uncommonly large subtest scaled-score ranges found in clinical
samples.
INTRA-INDIVIDUAL SUBTEST VARIABILITY
27
Table 2.1.Samples and Diagnoses
Sample N % N % Learning Disabilities 132 32.3
Psychiatric Disorders 178 38.1 Autism Spectrum Disorders 58 32.6 Conduct Disorders / Oppositional Defiant Disorders 36 20.2 Attachment Disorders 28 15.7 Attention Deficit and Hyperactivity Disorders 27 15.2 Tic Disorders 19 10.7 Depression 14 7.9 Other 66 37.1
Epilepsy 157 33.6
Seizure Type: Focal Onset / Localisation Related Seizures 87 55.4
Left Hemisphere 33 Right Hemisphere 24 Bilateral / Multifocal 32
Generalized Seizures 32 20.4 Uncertain whether Focal or Generalized 21 13.4 Unknown 17 10.8 Anti-epileptic Drug:
0 9 5.7 1 59 37.6
>1 63 40.1 n a 26 16.6
MRI positive data 29 18.5
Total 467 100
Methods
Participants
Participants were N = 467 children, aged six to 16 years, with FS-IQs > 75. Overall, 353
(76%) were male. The children were entitled to benefit from distinct special school
services in The Netherlands, according to national regulations (Resing et al., 2002). These
regulations describe the criteria for placement in different settings specialized in,
respectively, (specific) learning disabilities, childhood psychiatric disorders, or childhood
epilepsy. Generally, information from four sources is weighted by an independent
committee. These sources are the family of the child, the present school, a psychologist
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
28
who did the assessment (including the intelligence testing), and an educational,
psychiatric or medical specialist. For learning disabilities, specified criteria in terms of
academic failure must be met; for psychiatric disorders, a DSM-IV diagnosis from a
psychiatrist or qualified mental health psychologist is required; for epilepsy, a diagnosis
from a neurologist is required. In all cases, the difficulties caused by the diagnosed
condition must exceed the competencies of the regular school. Normal intellectual
abilities were a further criterion for the schools of the first two samples, but not the third
(epilepsy). As indicated, in this study FS-IQ was set to be above 75 for all. Co-morbidity
is a common phenomenon in childhood developmental disabilities and the samples are
diagnostically heterogeneous; the primary diagnosis as reflected by special school
placement was the criterion for inclusion in a sample. Diagnostic group membership –
type of special school – was the independent variable in this study. Demographic data are
presented in Table 2.1 and data on the Wechsler scales are shown in Table 2.2.
The first sample consisted of N = 132 children with (specific) learning disabilities
and the second sample consisted of N= 178 children with psychiatric disorders. The latter
group included children with neurodevelopmental disorders as well as children with
behavioral and emotional disorders related to major life events (e.g. traumas). The main
diagnoses of this sample were autism spectrum disorders (ASD), conduct disorders or
oppositional defiant disorders, reactive attachment disorders, attention deficit and
hyperactivity disorders, tic disorders, and depression. The subsamples are listed in Table
2.1. The percentages add up to over 100% due to psychiatric co-morbidity.
The third sample consisted of N = 157 children with seizure disorders. Mean age
at epilepsy onset was 5.6 years (ranging from the first day of life to age 15 years with SD
= 3.2). Mean duration of epilepsy was 4.0 years (SD = 3.2). Seizure type classification,
side of epilepsy onset, and information on medication and neuroimaging are presented in
Table 2.1.
Analyses
For each participant, subtest scaled-score range was calculated for five verbal, five
performance, and ten full-scale subtests. Verbal, performance, and full-scale subtest
scaled-score range was the dependent variable in the study. Mean scores are given in
Table 2.3; z-converted means are depicted in Figure 2.1. Levene’s testing for homogeneity
of variances showed no significant values for ANOVA or ANCOVA. ANOVA revealed
differences in mean age (age was higher in learning disabilities compared to epilepsy) and
mean PIQ (but not VIQ or FS-IQ), indicating higher PIQ in the samples of children with
INTRA-INDIVIDUAL SUBTEST VARIABILITY
29
learning disabilities and psychiatric disorders compared to the sample of children with
epilepsy. Also, chi-square showed that boys and girls were unevenly distributed among
the samples; significant differences were found, indicating that the rate of boys was
higher in the sample with psychiatric disorders than the sample with epilepsy. Three
separate ANCOVA’s were undertaken (for verbal, performance and full-scale subtest
scaled-score range), controlling for the pre-existing differences in PIQ, age, and sex. With
multiple, one-sided, one-sample t-tests, the observed clinical values were compared to the
estimated expected values (A. B. Silverstein, 1987), and effect sizes were calculated
accordingly (Cohen, 1988, p. 45). Overall, alpha was set at .05 and Bonferroni corrections
were used to control for family-wise errors. With chi-square, rates of children with
uncommonly high subtest scaled-score range (verbal scale: ≥ 8 points; performance scale
≥10, and full scale ≥11), expected in ~5% of the normal population (A.B. Silverstein,
1989), were compared to this value. As this value was seen as an approximation only,
alpha was set to .001. Rates of uncommonly high subtest scaled-score range were also
compared between the clinical samples.
Results
Verbal Scale
Comparison of means. Table 2.3 presents the expected mean subtest scaled-score
range for the verbal scale (mean = 4.7, SD = 1.7) and the observed valued for the distinct
samples, together with the results of the one sample t-tests, and Figure 2.1 depicts the z-
converted values of subtest scaled-score range. No differences were found between the
mean expected values and the observed values of the total sample or any of the distinct
clinical samples (Table 2.3). No differences were found between the means of the clinical
samples (ANCOVA: F(2,462) = 0.138, p = .871, n.s).
Rates of uncommonly large ranges. Large ranges (≥ 8 points) were found,
respectively, in 8.3%, 12.9% and 15.3% of the children with learning disabilities,
psychiatric disorders, and epilepsy. Compared to the expected rates, these values reached
significance for psychiatric disorders (Χ2 = 23.51, p < .001) and epilepsy (Χ2 = 34.97, p <
.001). Chi-square revealed no difference in the distributions of uncommonly large ranges
between the clinical samples. There was an almost twofold rate (likelihood ratio 1.8, 95%
Confidence Interval [CI]: 0.93 – 3.6) of children with epilepsy versus children with
learning disabilities.
CO
GN
ITIV
E PA
TTER
NS
IN P
AED
IATR
IC E
PILE
PSY
Tabl
e 2.
2.M
ean
Age
, Mea
n W
ISC
-RN
L IQs a
nd S
ex fo
r Thr
ee C
linic
al S
ampl
es a
nd T
wo
Subs
ampl
es (A
utis
m S
pect
rum
D
isor
ders
and
Lef
t Hem
isph
ere
Ons
et S
eizu
res)
A
ge (y
rs)
V
IQ
PI
Q
FS
-IQ
Boy
s M
SD
M
SD
M
SD
M
SD
N
N
%
&
rang
e
& ra
nge
&
rang
e
& ra
nge
Lear
ning
Dis
abili
ties
132
91
68
.9
12
.8a 1
.3
93
.3
10.8
97.3
12
.3
94
.6
10.8
7.
6 to
15.
6 72
to 1
19
68 to
125
77
to 1
24
Psyc
hiat
ric D
isor
ders
17
8 15
9 89
.3a
10.9
2.
7 93
.8
11.5
95
.7
13.5
93
.9
10.7
6.
0 to
16.
7 70
to 1
32
61 to
130
76
to 1
27
Aut
ism
Spe
ctru
m D
isor
ders
58
56
96
.6
9.9
2.3
96.2
12
.2
96.6
14
.2
95.6
11
.2
6.2
to 1
5.1
70 to
132
61
to 1
24
76 to
127
Ep
ileps
y 15
7 10
3 65
.6
9.7
2.7
95.3
12
.1
91.0
b 11
.9
92.5
10
.7
6.2
to 1
6.7
71 to
134
66
to 1
35
76 to
125
Le
ft H
emis
pher
e Se
izur
es
33
26
78.8
9.
5 2.
5 96
.6
11.1
89
.1
11.7
92
.3
8.7
6.3
to 1
6.1
71 to
120
66
to 1
20
78 to
108
To
tal
467
353
75.6
11
.1
2.7
94.2
11
.5
94.6
12
.9
93.6
10
.8
6.
0 to
16.
7
70 to
134
61 to
135
76 to
127
Not
e. a si
gnifi
cant
ly h
ighe
r tha
n ch
ildre
n w
ith e
pile
psy.
b . S
igni
fican
tly lo
wer
than
chi
ldre
n w
ith le
arni
ng d
isab
ilitie
s and
psy
chia
tric
diso
rder
s
30
INTRA-INDIVIDUAL SUBTEST VARIABILITY
31
Performance Scale
Comparison of means. The estimated expected mean subtest scaled-score range
was 5.8 (SD = 2.4). The total sample and the children with psychiatric disorders differed
from the expected value (Table 2.3). Significant differences were also suggested among
the clinical samples (ANCOVA: F(2,461) = 3.024, p = .050, partial η2 = .01). However,
pair-wise comparisons between the clinical samples did not yield significant results.
Rates of uncommonly large ranges. Large ranges (≥ 10 points) were found in
7.6%, 12.9%, and 7.0% of the children with, respectively, learning disabilities,
psychiatric disorders, and epilepsy. Compared to expected values, these percentages were
elevated for psychiatric disorders only (Χ2 = 23.51, p < .001). Chi-square revealed no
significantly different rates among the samples. Likelihood ratios were 1.7 (95% CI: 0.8
– 3.5) and 1.8 (95% CI: 0.93 – 3.7) for children with psychiatric disorders versus,
respectively, children with learning disabilities and children with epilepsy.
Full Scale
Comparison of means. The expected mean subtest scaled score range was 7.3 (SD
= 2.1). Values differing significantly from expected were found for the total sample and
the sample with psychiatric disorders (Table 2.3). Significant differences were found
between clinical samples (ANCOVA: F(2,462) = 4.130; p = .017, partial η2 = .02);
specifically, subtest scaled-score range was higher in children with psychiatric disorders
than in children with epilepsy.
Rates of uncommonly large ranges. Larges ranges (≥ 11 points) were found,
respectively, in 6.8%, 12.9%, and 10.8 % of the children with learning disabilities,
psychiatric disorders and epilepsy. Compared to expected values, significant differences
were found for psychiatric disorders (Χ2 = 23.51, p < .001) and epilepsy (Χ2 = 11.23, p =
.001). Again, differences of the large ranges between samples showed a trend that did not
reach statistical significance. Notably, however, there was almost a twofold rate
(likelihood ratio 1.9, 95% CI 0.9 - 4.0) for children with psychiatric disorders compared
to children with learning disabilities.
Subsamples
Although it is beyond the scope of this paper to enter into detail on all subsamples,
two subsamples were identified as showing conspicuously elevated subtest scaled-score
ranges relative to expected values: (a) from the sample with psychiatric disorders, the
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
32
subsample with autistic spectrum disorders (ASD, N = 58) was identified; and (b) from
the epilepsy sample, children with left focal onset seizures (LH; N = 33) were identified.
Data on these subsamples are also included in Table 2.2, Table 2.3, and Figure 2.1.
Table 2.3 shows that the ASD sample had a significantly larger subtest scaled-
score range than the expected values on the verbal scale, performance scale and full
scale, all with moderate effect sizes. When the ASD sample was compared to the other
children with diagnoses of psychiatric disorders (N = 120) in the psychiatric sample,
mean subtest scaled-score range was elevated for the ASD sample on the verbal scale
(t(1,94.7) = 2.49, p = .014, ES = 0.5) and the full scale (t(1,176) = 2.44, p = .016, ES =
0.4). Uncommonly large ranges were found for the verbal scale in 20.7% of these
children, for the performance scale in 15.5%, and for the full scale in 17.2%. The
percentages were significantly elevated when compared to expected values for the verbal
scale (Χ2 = 30.06, p < .001), performance scale (Χ2 = 13.51, p < .001), and full scale (Χ2 =
18.3, p < .001). Classificatory statistics revealed that when the ASD group was contrasted
to the others children with psychiatric disorders, uncommonly large ranges had
classificatory utility for the verbal scale: sensitivity was 21%, specificity was 91%,
Positive Predictive Power (PPP) was 52%, Negative Predictive Power (NPP) was 70%,
and likelihood ratio was 2.26 (95% CI 1.06 - 4.81). These values indicated that when a
child with psychiatric disorders is found to have a subtest scaled-score range of 8 or more
points on the verbal scale, it will more likely belong to the group with autistic spectrum
disorders.
Within the sample of children with psychiatric disorders, none of the other
subsamples showed elevated subtest scaled-score range consistently on all scales.
However, two subsamples of children with neurocognitive developmental disorders
showed elevations on one scale—specifically, the subsample with conduct disorders had
substantial scatter on the performance scale and the subsample with tic disorders had
elevated scatter on the verbal scale. These data merely suggest hypotheses for future
study, but are not presented here because many children had multiple diagnoses and the
sample sizes were too small to permit meaningful analyses.
INTRA-INDIVIDUAL SUBTEST VARIABILITY
33
Figure 2.1. Mean z-Converted Uncorrected Subtest Scaled-score Range Values and SEM-
bars for Verbal (black), Performance (white) and Full Scales (patterned), for Three
Clinical Samples and Two Subsamples
Table 2.3 shows that mean scaled-score range was higher than expected in the sample of
children with left hemisphere seizures. Significantly elevated values were seen for the
verbal scale (small effect size) and the full scale (moderate effect size). Such elevations
were not seen in the other epilepsy subsamples; planned comparisons indicated that
values were significantly different compared to the right hemisphere seizure-group for the
verbal scale (t(1,137) = 2.05, p = .042, ES = 0.3). Large range was found for the verbal
scale in 21.2% of these LH children, for the performance scale in 6.1%, and for the full
scale in 15.2%. The percentages were significant when compared to expected values for
the verbal scale only (Χ2 = 18.26, p < .001). Classificatory statistics revealed that when
the left hemisphere seizure-group was contrasted to the right hemisphere seizure-group,
there was a clear trend to find more children with uncommonly large ranges in the verbal
scale in the left hemisphere seizure-group. The valued failed to reach significance due to
lack of statistical power: sensitivity was 21%, specificity was 96%, PPP was 88%, NPP
was 47%, and likelihood ratio was 6.19 (95% CI 0.67 - 38.7).
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Total LearningDisabilities
PsychiatricDisorders
AutismSpectrumDisorders
Epilepsy LeftHemisphere
Seizures
z-sc
ore
Verbal range
Performance range
Full Scale range
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
34
Discussion
The assertion that intra-individual variability is elevated in pathology, taken for granted
by some researchers and opposed by others, was the subject of analysis in this study,
which focused on subtest scaled-score range for clinical samples of children with learning
disabilities, psychiatric disorders, and epilepsy. Analyses were conducted at three levels.
At the broadest level, 467 children from three categories of developmental
disabilities (learning disabilities, psychiatric disorders, and epilepsy) were compared to
the expected (“normal”) values. Significant elevations were found in the performance and
full scales – with conspicuously small effect sizes. This finding suggested that the study
was profiting from the effects of a relatively large sample size and also that possible
meaningful information was being masked by focusing on the heterogeneous total group.
At the second level of analysis, each of the clinical samples was compared to the expected
values and to each other. The sample with psychiatric disorders showed significantly
more than normal intra-individual variability on both the performance and full scales.
Also, the sample with psychiatric disorders showed more variability than the sample with
epilepsy on the full scale. Effect sizes were larger than for the total clinical sample, but
were still small. At the third and most specific level of analysis, two homogeneous
subsamples were subjected to further scrutiny. It appeared that the sample with ASD
(within psychiatric disorders) and the sample with focal LH seizures (within epilepsy)
showed elevated scatter, compared both to the expected values and to the other children
in their respective clinical original samples. For ASD, this was true for all three scales
(moderate effect sizes). For left hemisphere epilepsy, this was true for the verbal scale
(small effect size) and the full scale (moderate effect size).
When evaluating the percents of children with uncommonly large ranges, children
with (specific) learning disabilities did not display any unusual elevations relative to
groups of normal children. However, rates were increased in children with psychiatric
disorders on all scales, specifically for the ADS subsample. Rates were also increased for
the sample of children with epilepsy on the verbal and full scales, more clearly so in the
subsample of LH seizures.
INTR
A-I
ND
IVID
UA
L SU
BTE
ST V
AR
IAB
ILIT
Y
Tabl
e 2.
3.Su
btes
t Sca
led-
scor
e R
ange
: Mea
ns, S
Ds,
t-val
ues,
Prob
abili
ties a
nd E
ffec
t Siz
es fo
r Thr
ee S
ampl
es a
nd T
wo
Subs
ampl
es
cont
rast
ed to
Exp
ecte
d V
alue
s.
Ver
bal S
cale
Pe
rfor
man
ce S
cale
Fu
ll Sc
ale
Sam
ple
n M
SD
ta
p ES
M
SD
ta
p ES
M
SD
ta
p ES
Ex
pect
ed V
alue
b 4.
7 1.
7 5.
8 2.
1 7.
3 1.
9 Le
arni
ng D
isab
ilitie
s 13
2 4.
8 2.
0 0.
52
ns
- 5.
8 2.
3 0.
37
ns -
7.
4 2.
1 0.
28
ns
-
Psyc
hiat
ric D
isor
ders
17
8 5.
0 2.
0 1.
84
ns
- 6.
5 2.
6 3.
77
<.00
1 0.
3 8.
1 2.
2 4.
83
<.00
1 0.
4 A
utis
m S
pect
rum
D
isor
ders
58
5.
6 2.
3 2.
85
.003
0.5
6.
8 2.
8 2.
74
.004
0.5
8.
7 2.
3 4.
48
<.00
1 0.
7
Epile
psy
157
5.0
2.0
1.76
ns
-
6.0
2.4
1.10
ns
-
7.7
2.3
1.81
ns
-
Left
Hem
isph
ere
Seiz
ures
33
5.
3 2.
2 1.
64
.050
0.4
6.
5 2.
4 1.
63
ns -
8.
4 2.
0 3.
00
.003
0.6
Tota
l
46
7
4.9
2.0
2.45
ns
-
6.
1 2.
4 3.
28
.001
0.2
7.8
2.2
4.19
<.
001
0.2
Not
e. a O
ne sa
mpl
e t-t
ests
for c
ompa
rison
s aga
inst
est
imat
ed (e
xpec
ted)
val
ue. O
ne ta
iled-
test
, d.f.
= N
-1 in
all
case
s b E
stim
ated
exp
ecte
d va
lues
acc
ordi
ng to
Silv
erst
ein
(198
7)
35
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
36
The fact that the performance scale and not the verbal scale yielded the significant
differences in the primary samples of this study is consistent with a diverse body of
neuropsychological literature that has shown Wechsler’s performance subtests to be more
sensitive to brain injury and brain dysfunction than Verbal subtests (Kaufman &
Lichtenberger, 2006, chapters 8 and 9). Nonetheless, the present study suggests that
elevated subtest scaled-score range can also be seen on the verbal scale in specific
samples.
For children with (specific) learning disabilities, no elevations were found on any
measure. These results are in line of earlier studies (Flanagan & Kaufman, 2009; Watkins
et al., 2005). For children with ASD, elevated intra-individual variability has been
reported earlier (Joseph, Tager-Flusberg, & Lord, 2002). For children with epilepsy, to
the authors’ knowledge, no such studies have been reported, but the results are in line
with the large VIQ > PIQ discrepancies reported for children with unilateral focal onset
epilepsy (van Iterson & Augustijn, 2006) regardless of side of seizure onset. The
elevations in subtest scaled-score range on the verbal scale in left hemisphere epilepsy
may be the result of plasticity in the developing brain (Vicari et al., 2000).
The results found for children with ASD and children with epilepsy are interesting
in the light of recent research on the commonalities underlying both conditions and the
findings of high rates of subclinical EEG abnormalities in children with ASDs even in the
absence of manifest clinical seizures (Spence & Schneider, 2009).
Effect sizes increased when the selected samples were more homogeneous,
suggesting that specific samples of children with developmental disabilities may show
elevated intra-individual variability while others may not, or may even show decreased
variability. Thus, studies of subtest scaled-score range and their interpretation should take
into account type of pathology.
Scaled-score range was not found suitable for classification purposes between the
large samples; 95% confidence intervals for likelihood ratios were non-significant,
though some trends could be found. This is not surprising as scatter is a non-specific
measure which does not provide an answer as to where the variability is coming from, or
if it follows some specific pattern.
Classificatory statistics applied on selected samples indicated that an uncommonly
large range in the verbal scale was more likely to belong to a child with ASD and not a
child with “another diagnosis” within psychiatric disorders. Also the data suggest that
uncommonly large variability on the verbal scale may be characteristic of children with
INTRA-INDIVIDUAL SUBTEST VARIABILITY
37
left, but not right, hemisphere onset seizures; likelihood ratio was not significant,
probably due to small sample sizes. In line with the results of this study, and pertinent to
the discussion of the interpretability of scores beyond the summed scores of a scale
(Flanagan & Kaufman, 2009; Watkins et al., 2005), it is worth noting Saling’s (2009)
perspective. Based on the results of research within a highly specific area of research in
neuropathology – epilepsy surgery – Saling (2009), advocates against the use of scales of
summed scores in neuropsychological assessment of memory functions and argues in
favour of task specific measurement.
Intra-individual variability was studied with the WISC-RNL-version – which has
now been replaced by the WISC-IIINL, and by the WISC-IVUS/UK in English speaking
countries. The study of intra-individual variability is not confined to a specific version of
the Wechsler scales, but is applicable to any of Wechsler's scales and, in principle, to
subtest profiles yielded by different batteries as well. Flanagan and Kaufman (2009)
discuss the issue of inter-subtest variability within WISC-IV Factor Indexes. The
appreciation of a true difference between subtest scores depends on knowledge of the
relationship among the measures (i.e., the intercorrelations of the subtests) as well as the
frequency of occurrence of differences in the population studied.
CHAPTER 3
Differential effect of lesion side on intra-individual variability in children
with focal lateralized epilepsy
Loretta van Iterson
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
40
Abstract
A differential impact of hemispheric side (left versus right) on cognitive measures,
specifically verbal and performance IQ, has been described previously for both focal
onset seizures and lateralized brain lesions. This study revealed a differential effect on
intraindividual variability, measured as subtest scaled-score range, on the Dutch WISC-R
and WISC-III, in children with epilepsy. The presence of documented brain lesion was
associated with elevated variability on the verbal scale for the left hemisphere seizure
group and with decreased variability on the verbal and full scales for the right hemisphere
seizure group.
van Iterson, L. (2010). Differential effect of lesion side on intra-individual variability in children with focal lateralized epilepsy. Psychological Reports, 107(1), 113-119,
VARIABILITY IN LESIONAL FOCAL ONSET SEIZURES
41
Introduction
In childhood epilepsy, brain lesions affect cognitive outcomes differentially, depending
on age at time of lesion (Satz, Strauss, Wada, & Orsini, 1988), lesion type (Klein, Levin,
Duchowny, & Llabre, 2000; Mayes, Calhoun, & Crowell, 1998), the likelihood that
reorganization of brain functions has occurred (Blackburn et al., 2007; Liégeois et al.,
2004; Loring et al., 1999), and the actual presence of seizures (Deonna & Roulet-Perez,
2005). Effects of various measures, like degree of diffuseness versus circumscription of
brain lesion (Klein et al., 2000) and probability of showing IQ gains following epilepsy
surgery (Westerveld et al., 2000) have been shown to interact with lateralization (left
versus right hemisphere).
The literature cited studied elevation of IQ scales or the relationship between the
scales. Studies in epilepsy that focus on intra-individual subtest variability (subtest
scatter) are sparse. A recent study on subtest scatter (defined as scaled-score range – i.e.,
the difference between the person’s highest and lowest scaled score on a scale) was
conducted on large samples of children with Learning Disabilities, Psychiatric Disorders
and Epilepsy (van Iterson & Kaufman, 2009). The results were interesting. In some
samples of children with developmental disabilities (particularly the children with
Psychiatric Disorders) increased intra-individual subtest variability was a marker of
pathology, while in others (Learning Disabilities) it was not. Specifically, children with
Autism Spectrum Disorders and children with left hemisphere seizures were more likely
to show elevated variability. This elevation was not seen in children with right
hemisphere seizures, suggesting a differential impact of seizure lateralization on subtest
scaled-score range. These results raise the question whether this difference is related only
to hemispheric side of seizure onset or whether there is a role for brain lesion as well.
This study focuses on the impact of side of focal onset seizures on children’s subtest
scatter in the Dutch WISC-R and WISC-III in the light of the presence or absence of
documented brain lesion.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
42
Table3.1 Demographic data, epilepsy variables and results on Wechsler Scales for the four
samples
Hemispheric Side of Seizure Onset Left Right
Sample No lesion MRI lesion No lesion MRI lesion
N 34 22 19 15 Boys N (%) 22 64.7 16 72.7 10 52.6 8 53.3 Righthanded N (%) 29 85.3 17 77.3 15 78.9 14 93.3 Age (yr.) M (SD) 9.4 2.5 10.2 3.1 8.7 2.5 10.6 2.5
range 6.3 to 16.5 6.8 to 15.8 6.3 to 14.0 7.4 to 15.8 WISC version WISC-RNL N (%)
21 61.8 12 54.5 9 47.4 12 80 WISC-IIINL N (%)
13 38.2 10 45.5 10 52.6 3 20 Verbal IQ M (SD) 94.1 10.5 95.7 10.7 99.4 10.5 95.9 10.5
range 71 to 116 76 to 120 82 to 118 72 to 111 Performance IQ M (SD) 89.9 12.6 91.8 10.4 87.9 18.3 89.0 9.6
range 66 to 120 71 to 121 58 to 118 78 to 118 Full Scale IQ M (SD) 91.2 9.6 93.0 8.8 92.8 12.0 91.6 9.6
range 75 to 108 78 to 108 77 to 120 75 to 116 Age at onset of epilepsy M (SD) 5.2 3.1 6.6 3.7 5.6 2.8 5.3 4.5
range 0.3 to 14.8 0.5 to 16.2 0.1 to 10.3 0.3 to 14.5 Interval to Test (yrs) M (SD) 3.9 2.5 3.6 3.4 3.1 2.7 5.4 5.3
range 0.5 to 9.8 -3.2 to 12.3 0.4 to 10.0 -2.8 to 13.3 Antiepileptic Drugs M (SD) 1.6 0.6 1.7 0.8 1.6 0.8 1.1 0.9
range 1 to 3 1 to 4 0 to 3 0 to 3 Antiepileptic Drugs Tried M (SD) 2.2 1.4 2.6 1.5 2.3 1.6 1.5 1.1
range 1 to 7 1 to 5 1 to 7 0 to 4 Type of Lesion
Cortical dysplasia N (%) 5 22.7 5 33.3 Mesial Temporal Sclerosis N (%) 3 13.6 1 6.7 Tumor N (%) 4 18.2 2 13.3 Other N (%) 10 45.5 7 46.7
Surgery N (%) 6 27.3 4 26.7
Note. No statistically significant differences between the four samples are seen on any variable after ANOVA or chi-square testing.
Methods
Participants.
The sample consisted of 90 children with localization-related or focal onset
epilepsy (Engel, 2006), classified into four samples according to lateralization of seizure
onset (based on EEG and seizure semiology) and presence or absence of positive findings
on MRI (positive findings indicative of lesion versus no documented lesion). The
epilepsy data were available from neurological files and were collected independently
from the psychological data. The children were between six and 16 years of age (mean =
9.6 years, SD = 2.7); mean age at epilepsy onset was 5.7 years (SD = 3.5) and mean
VARIABILITY IN LESIONAL FOCAL ONSET SEIZURES
43
duration of epilepsy was 3.9 years (SD = 3.4). Except for five children, the participants
were on anti-epileptic drugs and 42 were on more than one anti-epileptic drug. Only two
children were candidates for epilepsy surgery; in eight more, brain surgery had occurred
earlier in life for various reasons, e.g., tumors or arteriovenal bleeding; in two, testing
antedated seizure onset. Children were selected as having full scale IQs of 75 or higher.
Table 3.1 presents the demographic data as well as epilepsy variables and Wechsler IQ.
Fifty four children were tested with the Dutch WISC-R (van Haasen et al., 1986)
and 36 with the WISC-IIINL (Wechsler, 2005). The two test versions can be considered to
measure the same construct (Wechsler, 1992, 2005) as correlations between the scales are
high, most clearly so for the verbal and full scales (.90 for the verbal scale, .81 for the
performance scale and .90 for the full scale, Wechsler, 2005, p. 70). Values of expected
subtest scatter were virtually identical for both test versions, when these were calculated
following Silverstein (1987; see also van Iterson & Kaufman, 2009). For the WISC-RNL
(WISC-IIINL in brackets) the mean value for scatter among 5 verbal scale subtests was 4.71,
SD = 1.8 (mean = 4.73, SD = 1.8); for scatter among 5 performance scale subtests, mean
= 5.77; SD = 2.1 (mean = 5.71, SD = 2.1); and among 10 full scale subtests, mean = 7.34,
SD = 1.9 (mean = 7.36, SD = 1.9).
Analyses
For each child and for each scale, subtest scaled-score range was converted into a
z-score. The results of multiple one-sample two-sided t-tests for the four groups, corrected
for family-wise errors, are presented in Figure 3.1.
Results
Analysis of variance and chi-square analyses revealed no differences in means or
proportions between the four samples on any demographic variable, any of the WISC-
variables or any epilepsy variable (Table 3.1). Verbal – Performance discrepancy
favoured the verbal scale in all samples; paired sample t-tests revealed significant
differences for the two right hemisphere samples only.
ANOVA showed significant differences on subtest scatter for (a) the verbal scale (F(3,86)
= 2.923, p = .038, eta squared (η2) = .09), indicating more variability in the MRI lesion
for the left than the right hemisphere group; and (b) the full scale (F(3,86) = 5.136, p =
.003, η2 = .15), indicating less variability in the MRI lesion than no lesion group (Figure
3.1).
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
44
Figure 3.1. Subtest scaled-score range for the Verbal
(black), Performance (white) and Full Scale (striped):
means and SE.
a = one-sample t-testing for difference from zero after Bonferroni correction, p < .05 b = within MRI lesion groups: different from Verbal scaled-score range in left hemisphere, p < .01 c = within MRI lesion groups: different from Full Scale scaled-score range in left hemisphere, p < .05 d = within right hemisphere: different from Full Scale scaled-score range in group without MRI-lesion group, p < .001
Subtest scatter for each scale was entered as the dependent variable and seizure side and
MRI findings as the independent variables in a MANOVA. This analysis yielded
significant results for both main effects as well as their interaction. Significant values
were seen for seizure onset side for the verbal scale (F(3,86) = 4.067, p = .047, partial eta
squared (η2) = .05) indicating more variability in the left hemisphere seizure group. For
the MRI findings, the effect was seen on the full scale (F(3,86) = 11.724, p = .001, partial
η2 = .12); variability was lower in children with MRI lesions. There was a significant
seizure side by MRI interaction for the verbal scale (F(3,86) = 6.017, p = .016, partial η2
= .07), indicating that in the presence of a lesion variability was elevated in the left and
Subtest scaled score-range
a, b
a, c, d
-1,2
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
1,2
no MRI lesion n = 34
MRI lesion n = 22
no MRI lesion n = 19
MRI lesion n = 15
Left Hemisphere Right Hemisphere
z-sc
ores
VARIABILITY IN LESIONAL FOCAL ONSET SEIZURES
45
lowered in the right hemisphere seizure group. Also, a side by lesion interaction was seen
on the full scale (F(3,86) = 6.228, p = .014, partial η2 = .07); the right hemisphere group
without lesions showed elevated variability, and the group with lesions showed decreased
variability.
Although it is not meaningful to break down the samples further according to test
version given the small samples sizes, when test version was included as a predictor
however, an interaction with MRI lesion for the performance scale was seen affecting
variability on the right hemisphere sample (higher in the children tested with the WISC-
IIINL).
Moderate test accuracy was revealed by the areas under the curve (AUC) when
receiver operating characteristics (ROC) were calculated in order to study data on
individuals. Within the right hemisphere group, the procedure indicated that lower values
on the full scale would more likely belong to children with a lesion (AUC = 0.807, p =
.002, 95% Confidence Interval (CI) = 0.66 to 0.96, a cut-off value of one standard
deviation below the mean (-0.97SD) had a sensitivity of 40% and a specificity of 89%).
Within the lesion groups, a higher value in the verbal scale would more likely belong to a
child in the left hemisphere seizure group (AUC = 0.829, p = .001, 95% CI = 0.70 to
0.96; for a cut-off of +1 SD, sensitivity was 37% and specificity was 100%). A lower
value on the full scale would more likely belong to the right hemisphere group (AUC =
0.739, p = .015, 95% CI = 0.58 to 0.90; for a cut-off of -0.97 SD, sensitivity was 40% and
specificity was 91%).
Discussion
The present study confirms the finding that variability is elevated in left but not right
hemisphere onset seizures (van Iterson & Kaufman, 2009), and goes beyond it indicating
that a differential effect can be seen which depends on the presence or absence of a brain
lesion. In the presence of a lesion, variability was found to be elevated in the left
hemisphere group in the verbal Scale, and lowered in the right hemisphere group on the
verbal and full scales.
The results add to the existing body of literature on the differential impact of
lateralized brain pathology on cognitive measures like level of performance on the verbal
and performance scales and the difference between the two scales (Loring et al., 1999;
Westerveld et al., 2000) extending the differential impact to inter-subtest variability. The
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
46
findings also suggest that brain pathology, besides leading to elevated variability –
possibly reflecting poorer “central coherence” (Noens & van Berckelaer-Onnes, 2005) –
may also manifest itself as lowered, or “less than normal” variability (A.S. Kaufman,
1979). These differences can possibly be understood as being mediated by functional
reorganization of the brain (Blackburn et al., 2007).
Significantly elevated variability was not seen on the performance scale. There are
several possible explanations. First, the overall lower performance than verbal scores (van
Iterson & Augustijn, 2006) may have been reflected in lower subtest scatter (Matarazzo,
Daniel, Prifitera, & Herman, 1988). Second, data on the participants were collected over a
prolonged period of time which can affect diagnostic decisions – that is to say, advances
in neuroimaging techniques enable detection of lesions which earlier remained undetected
and may increase the number of children classified as having a lesion. Furthermore,
changes in intelligence test version may potentially affect results on one scale or subtest
more than the other (Flynn, 2007; A. S. Kaufman, 2010). As stated, IQs yielded by the
two versions of WISC used in this study correlated substantially. However, the
performance IQs did not correlate as highly as the verbal IQs (.81 vs. .90), suggesting that
the performance scale, in particular, may measure slightly different constructs on the
Dutch WISC-R and WISC-IIINL. All of these factors may have contributed to the somewhat
inconsistent results seen on the performance scale between the earlier and more recently
included cases.
Elevated variability in cognitive function in children has implications for
educational practice, as children may surprise and startle psychologists, parents and
educators when showing high levels of proficiency on one area and low on another area,
even though the two areas seem quite similar in the cognitive abilities they assess. Thus,
knowledge of this variability is the first step to understanding and guiding these children
when planning their interventions.
CHAPTER 4
Establishing Reliable Cognitive Change in Children with Epilepsy:
The Procedures and Results for a Sample with Epilepsy
Loretta van Iterson
Paul B. Augustijn
Peter F. de Jong
Aryan van der Leij
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
48
Abstract
The goal of this study was to investigate reliable cognitive change in epilepsy by
developing computational procedures to determine Reliable Change Index scores (RCIs)
for the Dutch Wechsler Intelligence Scales for Children. First, RCIs were calculated
based on stability coefficients from a reference sample. Then, these RCIs were applied to
a sample of 73 children with refractory epilepsy who were tested twice with the WISC
after a mean interval of 2.3 years. Results indicated that children with refractory epilepsy
are at risk for cognitive decline over time: 26.0 percent of the children showed reliable
losses on verbal IQ and 16.4 percent on the full-scale IQ (expected rate = 5%). Declines
on performance IQ were within expected limits.
van Iterson, L., Augustijn, P. B., de Jong, P. F., & van der Leij, A. (2013). Establishing reliable cognitive change in children with epilepsy: The procedures and results for a sample with epilepsy. Journal of Psychoeducational Assessment, 31(5), 448-458.
RELIABLE COGNITIVE CHANGE
49
Introduction
It remains uncertain whether cognitive declines occur over time during the course of
epilepsy (Devinsky & Tarulli, 2002). Two updates on longitudinal studies of children
with epilepsy indicate that evidence of cognitive decline in epilepsy continues to be
sparse and inconclusive (Dodrill, 2004; Seidenberg, Pulsipher, & Hermann, 2007).
Numerous studies, in fact, report non-significant changes over time in groups of children
with epilepsy (Aldenkamp, Alpherts, De Bruine-Seeder, & Dekker, 1990; Bjornaes,
Stabell, Henriksen, & Loyning, 2001; Bourgeois, Prensky, Palkes, Talent, & Busch,
1983; Jones, Siddarth, Gurbani, Shields, & Caplan, 2010; Oostrom, van Teeseling,
Smeets-Schouten, Peters, & Jennekens-Schinkel, 2005; Rodin, Schmaltz, & Twitty,
1986). These findings of no significant decline, though encouraging, are generally
associated with non-referred samples of children with relatively uncomplicated epilepsies
and high rates of seizure remission (Jones et al., 2010; Oostrom et al., 2005). However,
severity of epilepsy syndrome – particularly epileptic encephalopathies and epilepsies
with a known (symptomatic) or suspected (cryptogenic) cause – has been associated with
(a) low IQ (Bulteau et al., 2000; Nolan et al., 2003) and (b) increasing deviation from the
developmental curve (Berg et al., 2004). Furthermore, within an epilepsy syndrome,
variability among individuals has been reported to be high (Reijs et al., 2006).
To gain insight into the heterogeneity within a clinical sample, and to increase the
clinical meaningfulness of research data beyond the study of group means, studies should
also provide information on individuals (Chelune, 2002; Martin et al., 2002). Studies of
children with epilepsy that do report rates of individuals with cognitive change are rare,
both in samples without (Rodin et al., 1986) or with epilepsy surgery (Westerveld et al.,
2000).
These occasional studies of cognitive change in children have rarely used the
psychometric properties of the test to determine reliable cognitive change.
In adult literature, approaches to quantify reliable cognitive change have used
normal or clinically stable samples as reference groups. From these references samples,
reliable change has been computed applying regression-based procedures (Cysique et al.,
2011; Martin et al., 2002) or, alternatively, the Reliable Change Index (RCI; G.J.
Chelune, Naugle, Lüders, Sedlak, & Awad, 1993; Woods et al., 2006). These RCI
formulas tend to perform as well as more complex regression formulas (Heaton et al.,
2001) and to be more “user friendly” (G. J. Chelune, 2002, p.424).The RCI is a cut-off
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
50
score and ±RCI yields a confidence interval (typically 90%). Descriptions of procedures
to establish RCI appear in the literature (G.J. Chelune et al., 1993; Maassen, Bossema, &
Brand, 2009). When retesting is conducted, “change scores” that fall within the
confidence interval are interpreted as “common” or non-meaningful differences; on the
contrary, change scores outside the interval are deemed uncommon or “reliable cognitive
changes.” RCI formulas are based on the standard deviations of the mean scores at first or
“baseline” testing (Chelune et al., 1993) or at both baseline and retesting (G.J. Chelune et
al., 1993; Maassen, Bossema, & Brand, 2009). The formulas are also based on the
stability coefficients. Where practice effects are known to occur, the RCI cut-off score is
adjusted accordingly (Chelune et al., 1993; Woods et al., 2006).
Woods et al. (2006) underscored the importance of examining the validity of this
RCI approach in clinical samples expected to present cognitive change, whether
improvement or decline. Although epilepsy is considered an ongoing neurological
condition that can potentially lead to cognitive change, these kinds of validation studies
barely exist in the epilepsy research on children. Thus, reliable cognitive change in
children with epilepsy was the focus of the present study.
Statistical properties, Test Familiarity and Test Version
Changes in IQs at retesting vary depending on familiarity with the test, length of the
interval between measurements(Kaufman, 1994), IQ at baseline (Schittekatte, 2005), and
test version used (Canivez & Watkins, 1998). A period of 9 to 12 months is often
considered sufficient to counter practice effects in referred children (Canivez & Watkins,
1998); however, some evidence exists that in children with epilepsy, the impact of
practice effects may level off within six months (Neyens, Aldenkamp, & Meinardi, 1999).
When the same test is administered at both measurements, long-term studies on referred
children without epilepsy – retested after a 2½ to 3-year interval – generally show stable
scores, with differences that are either not significant or not clinically meaningful
(Canivez & Watkins, 1998; Pesch & Ponsioen, 2004). These test-retest studies are based
on data from the WISC, the WISC-R, the Dutch WISC-R (to be called WISC-RNL), or the WISC-
III. Stability coefficients over time have been found to be high for the three IQ Scales that
comprise the various versions of the WISC, verbal scale (VIQ), performance scale (PIQ)
and full scale (FS-IQ) (Canivez & Watkins, 1998; Schittekatte, 2005). For the Index
scores, Canivez and Watkins (1998) also report high stability coefficients; for the Dutch
RELIABLE COGNITIVE CHANGE
51
Wechsler tests, however, coefficients were based on small numbers of children
(Schittekatte, 2005) and are of limited utility.
Purpose
In order to establish the rate of children with epilepsy who show reliable cognitive
change, the present study focused on the three IQ Scales of the Dutch Wechsler
Intelligence Test for Children in a sample of referred children with refractory epilepsy
who were tested twice. The following research questions were addressed:
(1) Given the presence of two sets Wechsler IQs obtained at Time 1 (T1) and Time 2
(T2), what magnitude of T1–T2 change is sufficient to denote a significant
change, that is, a “reliable cognitive change”?
(2) How often do these reliable changes occur in children with epilepsy, and is this
percentage larger than expected?
The principal hypothesis is that referred children with epilepsy show decline over time in
cognitive function (Seidenberg et al., 2007). It is hypothesized that the study of
individuals will uncover elevated rates of children who demonstrate reliable cognitive
decline during the course of epilepsy relative to a clinical control reference sample
without epilepsy.
The study included two phases: (1) testing the utility of reliable change formulas
and establishing RCIs (90% confidence intervals) based on Dutch Wechsler test-retest
data from a reference sample; and (2) applying the RCIs to establish the proportion of
children with epilepsy who show reliable cognitive changes at retesting. Phase 1 is
described in the Method section with Phase 2 presented in Results.
Methods
Participants
The sample comprised 73 Dutch children who met the following criteria: (a) they had a
diagnosis of epilepsy, (b) they were tested (T1) with either the WISC-RNL (van Haasen et
al., 1986) or WISC-IIINL (Wechsler, 2005) (c) they were retested (T2) after an interval of 12
months or more on the same test version that was used at T1, and (d) no epilepsy surgery
had occurred in between measurements. The children presented at a Dutch tertiary centre
for epilepsy or at a special school providing services for children with epilepsy. Reasons
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
52
for testing were concerns expressed by parents, schools, or neurologists about the child’s
cognitive development. Wechsler testing generally occurred as part of a comprehensive
neuropsychological evaluation and took place at the start of the evaluation or after a
break. Commonly, results of the assessment were used in applications for special
financial and educational services for the child. Thus, this study dealt with a selected
sample of referred children who were tested with the same WISC on both occasions, either
the WISC-RNL or the WISC-IIINL. No exclusionary criteria were applied in terms of epilepsy
type or IQ at T1.
The sample was selected from 420 children with epilepsy who had completed a
WISC. Of these, 290 children had been tested once. From the 130 children who had been
tested twice, 57 were not eligible because: (a) retesting had taken place within twelve
months (n = 23), (b) epilepsy surgery was conducted (n = 2), or (c) different versions of
the Dutch WISC were administered at T1 and T2 (n = 32). The selected sample (n = 73)
and the sample of children who were not eligible for this study (n = 347) were overall
similar. ANOVAS or chi-square tests using an alpha level of .01 to control for familywise
error rates, showed that the samples did not differ in sex, handedness, VIQ, PIQ or FS-IQ,
age at onset of epilepsy, duration up to first testing, number of anti-epileptic drugs (AED)
used, seizure type, severity of epilepsy syndrome, or rates of children with documented
MRI-abnormalities. The children of the sample of interest, however, were tested for the
first time at a younger age (selected sample: mean = 9.1, SD = 2.2; not selected sample:
mean = 10.2, SD = 2.8). The difference in age was significant, F (1,419) = 9.84, p = .002,
r = .15.
Table 4.1. Epilepsy variables at time 1 (T1) and time 2 (T2)
Mean SD Range
Age at onset of epilepsy (yrs) 5.4 3.0 0.1 to 13.2 Age at T1 (yrs) 9.1 2.2 6.0 to 15.9 Age at T2 (yrs) 11.4 2.3 7.8 to 16.9 Duration epilepsy to T1 (yrs) 3,7 3.0 0.2 to 13.4 Duration epilepsy to T2 (yrs) 6.0 3.1 1.6 to 15.8 Interval T1 T2 (yrs) 2.3 1.2 1.0 to 7.5 Anti-epileptic drugs used at T2 (n = 65) 1.3 0.8 0 to 4 Anti-epileptic drugs tried at T2 (n = 65) 2.3 1.3 0 to 5 Epilepsy syndrome severity, min = 1, max = 10 (n = 70) 6.0 1.5 2 to 8
RELIABLE COGNITIVE CHANGE
53
Test Versions
Comparison of the 41 children tested twice with the WISC-RNL with the 32 children
tested twice with the WISC-IIINL revealed overall equality. ANOVA and chi-square (with
alpha set at .01) revealed no significant differences between the subsamples on any IQ
Scale at T1 or T2; on T1–T2 change in IQ; on any demographic variable, namely sex,
handedness, age at T1 or age at T2; or on any epilepsy variable (age at onset, duration of
epilepsy up to T1 or T2, T1–T2 time interval, number of AEDs used, seizure type,
epilepsy syndrome severity, seizure status at T2, MRI status).
Table 4.2.Demographic and epilepsy variables
N %
Sex Boys 38 52.1 Girls 35 47.9
Handedness (n = 63) Right-handed 54 84.1 Left-handed 9 15.9
Seizure type and side Focal 51 69.9
Left hemisphere 24 32.9 Right hemisphere 12 16.4 Bilateral or multifocal 15 20.5
Generalized 6 8.2 Uncertain 11 15.1 Unknown 5 6.8
MRI+ (positive findings) 21 28.8 Seizure status
Active epilepsy 41 56.2 Inactive epilepsy 16 21.9 Uncertain 7 9.6 Unknown 9 12.3
Epilepsy Variables
The characteristics of the sample are presented in Table 4.1 and Table 4.2. Data collected
on epilepsy relate to age at onset of epilepsy; side of seizure onset (left hemisphere onset,
right hemisphere onset, or bilateral seizures); type of seizures (focal versus generalized);
presence of documented brain lesion on neuroimaging (MRI+); and epilepsy syndrome
severity, classified according to the 10-point Syndrome Severity Scale for Children with
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
54
Epilepsy (ESSS-C, Dunn, Buelow, Austin, Shinnar, & Perkins, 2004). Seizure status was
divided in active or inactive epilepsy; inactive epilepsy was defined as seizure freedom at
T2 of 12 months or longer.
Clinical Control Reference Sample
The use of a referral or clinical control sample rather than a sample of normal controls in
clinical studies has been recommended (Cysique et al., 2011; Woods et al., 2006).
Following this line, to calculate RCI values for the Dutch Wechsler tests, the referral
sample from the test manual (Wechsler, 2005), based on the study of Schittekatte (2005)
was used as a clinical control reference sample.
Table 4.3. Reliable Change Indexes (RCI) based on coefficients of stability RT1T2
American WISC-III Dutch WISCs Observed Expected according to Based on Schittekatte Wechsler Scale Canivez Chelune Maassen RT1T2 RCI RCI RCI RT1T2 RCI
VIQ .87 13 13 13 .87 14 PIQ .87 14 - 15 14 15 .81 18 FS-IQ .91 11 11 11 .88 14
Schittekatte’s study appears as a valuable reference for the present purpose. It is
based on a large sample of 353 children tested with the Dutch Wechsler tests with a mean
interval between testings of 3 years and a mean FS-IQ in the Low Average range (82) at
T1. The sample consisted of children referred for cognitive and learning problems. One
major advantage of the clinical control reference sample is that it does not primarily relate
to children with ongoing neurological conditions possibly associated with cognitive loss.
The previous study differs from the present study in two main aspects – in Schittekatte’s
study (a) a change in test version had taken place from WISC-RNL at T1 to WISC-IIINL at T2,
and (b) the T1–T2 time interval was longer (mean difference of 7.9 months) and had a
wider range (range 0.2 to 8.7 years). Both differences, changes in test version and longer
time intervals, lead to lower stability scores (Sattler, 2001). Therefore, the reference
sample is likely to yield conservative RCI estimates and, therefore, less likely to lead to
type I errors.
Thus, a neurologically “uncomplicated” clinical control reference sample provides
the standard for evaluation of the neurologically compromised sample. If elevated rates of
RELIABLE COGNITIVE CHANGE
55
decline are found, as hypothesized, they are more likely to be related to the ongoing
epileptic condition itself and less to other factors like familiarity with the test or non-
specific effects of remediation programs.
Coefficients of stability: comparison between the reference sample and the sample
with epilepsy
For the reference sample, Schittekatte (2005) reports coefficients of T1–T2
stability of .87, .81, and .88 respectively for the verbal, performance and full scales (see
Table 4.3). For the sample with epilepsy, these values were .75, .76, and .77. The
coefficients for the sample with epilepsy were significantly lower than the values found
by Schittekatte for the verbal and full scales, but not the performance scale (using
Fisher’s z for independent samples and alpha set at p < .01 to control for multiple
comparisons).
Establishing and testing Reliable Change Index (RCI) formulas
Similar to Schittekatte (2005), Canivez and Watkins (1998) collected a large
sample of referred children tested twice with the WISC-III. The sample included 667
American children tested for special education eligibility. The authors calculated
coefficients of stability for the VIQ, PIQ and FS-IQ. Notably, the authors also constructed
an empirically-derived base-rate table that presented frequencies of changes in IQ
(Canivez & Watkins, 1998, p.289). These data provided an excellent starting point to test
RCI formulas.
Chelune et al. (1993, pp 45-46) published a RCIs formula based on the
coefficients of stability and the standard deviation from the first test administration.
Maassen et al. (2009, p.340, formula 2) expanded the formula to include the standard
deviation of both the first and second testings. To compare the formulas from the two
studies, they were applied to the coefficients of stability from Canivez and Watkins
(1998, p.287). As can be seen in Table 4.3, both formulas provided accurate estimates:
they yielded RCI values within 1 point of those found empirically by the authors (Canivez
& Watkins, 1998, p.289). In the present study, the formula from Maassen et al. was
applied as follows:
SEDIFF = SDT12 SDT 2
2 1 RT1T 2
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
56
where SDT12 and SDT2
2 are the squared standard deviations at T1 and T2, respectively, and
RT1T2 is the stability coefficient between measurements at T1 and T2. The value for the
90% confidence interval: ± (SEDIFF * 1.64).
Cut-off values for the RCIs
After applying the formula to Schittekatte’s coefficients of stability, the
established RCI cut-off values were 14 for VIQ, 18 for PIQ, and 14 for FS-IQ (rounding
off to the nearest integer). The last two columns of Table 4.3 present the stability
coefficients and the RCIs used to establish reliable change in the present study. Chelune
et al. (1993) suggested that the differences between T1 and T2 be adjusted with the T1–
T2 changes found in the reference sample to control for practice effects. Given the
intertest interval of at least 12 months for the present sample, no practice effects were
expected.
Analyses
Temporal stability (mean change) was calculated with paired-samples t tests for the IQ
scales. Two-tailed tests were applied, alpha was set to .01 to correct for multiple
comparisons, and r was used as a measure of effect size; r >= .5 was interpreted as a large
effect size (Field, 2005). The percentage of children with epilepsy outside the 90%
reliable-change interval was established. For each scale, a single chi-square statistic was
used to test whether the observed rates for no reliable change (±RCI), gains (>= +RCI),
and losses (<= –RCI) differed from: the expected rates of 90% (“normal gain or loss”),
5% (“reliable gain”), and 5% (“reliable loss”). Again, alpha was set at .01.
Results
Changes over Time on Wechsler Scales
As presented in Table 4.4, mean decline between T1 and T2 on the verbal scale
was 7.2 IQ points; a paired-sample t test indicated this was significant (p < .001, effect
size r = .5). The performance scale showed a non-significant decline of 0.6 IQ points. The
full scale showed a significant decline of 4.4 IQ points (p = .001, r = .4). Changes ranged
from –52 (loss of 52 IQ points) to +21 (gain of 21 points) on the verbal scale, –38 to +29
on the performance scale and –48 to +17 on the full scale.
RELIABLE COGNITIVE CHANGE
57
Reliable Cognitive Change in Epilepsy
Table 4.4 presents the rates of children showing reliable gains and losses, as well
as the results of the chi-square analyses for the three IQ scales. The table shows that the
percent of children who displayed a reliable gain on any IQ scale is close to the expected
5%, while the rate of children with significant loss is elevated in relation to the expected
5% on the verbal and full scales. On the verbal scale, 53 children (72.6%) presented with
T1–T2 differences within the reliable change interval; one child (1.4%) showed a reliable
gain; and a substantial 19 children (26.0%) showed a reliable loss. The proportions
differed significantly from the expected values (Χ2 = 68.93, p < .001). The values found
for the performance scale – gain in four children (5.5%) and loss in four children (5.5%)
– were not different from those expected (Χ2 = 0.07, p > .01). On the full scale, two
children (2.7%) showed a gain and 12 (16.4%) a loss; values were significant (Χ2 =
20.53, p < .001).
Discussion
The aim of this study was to establish reliable cognitive change in children with epilepsy
tested twice with the WISC-RNL/WISC-IIINL, applying a 90% RCI. The present study differed
from earlier research on cognitive change in child epilepsy (Aldenkamp, Alpherts, De
Bruine-Seeder, & Dekker, 1990; Westerveld et al., 2000) as it considered the
psychometric properties of IQ changes on the Wechsler Scales to predetermine RCIs. It
was shown that observed changes could be predicted with great accuracy, providing
support for the usefulness of these formulas to determine cognitive change and
confirming that empirical data on change can be estimated from psychometric data
(Chelune et al., 1993; Maassen et al., 2009). Lower coefficients of stability found in the
sample with epilepsy compared to the reference sample suggested more variability in IQ
scores in children with epilepsy from T1 to T2. Indeed, compared to the expected values
of 5%, elevated rates of reliable loss at T2 were seen in epilepsy on the verbal scale
(26.0%) and on the full scale (16.4%). Rates for gains did not exceed expected values on
any IQ-scale. The present data support the hypothesis that the seizure condition is
associated with an elevated risk for cognitive decline (Dodrill, 2004; Seidenberg,
Pulsipher, & Hermann, 2007). Notably, this decline is only seen on the verbal and full
scales and not on the performance scale.
C
OG
NIT
IVE
PATT
ERN
S IN
PA
EDIA
TRIC
EPI
LEPS
Y
Tabl
e 4.
4. W
echs
ler I
Q sc
ores
at T
1 an
d T2
and
cha
nge
over
tim
e (
T2-T
1) w
ith p
aire
d-sa
mpl
es t
test
and
per
cent
ages
of c
hild
ren
show
ing
relia
ble
gain
s, no
relia
ble
chan
ge a
nd re
liabl
e lo
sses
IQ
at T
1
IQ a
t T2
Δ
T1 -T
2
Rel
iabl
e ch
ange
Mea
n SD
Mea
n SD
Mea
n SD
t
p ES
( r )
Gai
n (5
%)
n.s.
(90%
) Lo
ss
(5%
) Χ
2 p
VIQ
90.8
9 15
.5
83
.68
16.3
7.21
11
.3
5.43
5 <
.001
.5
1.4
72.6
26
.0
68.9
93 <
.001
PI
Q
87
.45
16.3
86.8
6 17
.8
0.
59
11.9
0.
423
n.s.
-
5.5
89.0
5.
5 0.
075
n.s.
FS-IQ
88.1
0 15
.9
83
.73
17.0
4.37
11
.1
3.35
8 .0
01
.4
2.
7 80
.8
16.4
20
.531
<.0
01
58
RELIABLE COGNITIVE CHANGE
59
The differential changes for the verbal and performance scales may be partially
related to the higher initial VIQ than PIQ. It is unlikely, however, that they were related
to regression to the mean phenomenon (Heaton et al., 2001), which more often affects
scores in the extremes, because the present sample had low average IQs at first testing. A
VIQ > PIQ difference has been reported regardless of seizure onset side (van Iterson &
Augustijn, 2006). A somewhat more favourable course of the performance scale than the
verbal scale at retesting has also been described in samples without epilepsy (Kaufman,
1994; Schittekatte, 2005), as well as in children who have undergone epilepsy surgery
(Westerveld et al., 2000), and is possibly due to the decreased “novelty” of the
performance tasks at T2 (Canivez & Watkins, 1998).
Epilepsy variables
Seidenberg et al. (2007) state that there is a dearth of studies on cognitive decline,
particularly studies that include epilepsy variables. The breakdown of the present sample
according to epilepsy variables yielded small subsample sizes, and did not permit
conducting meaningful analyses. Earlier longitudinal studies on children with epilepsy
suggested that patterns of change were independent of seizure laterality (Westerveld et
al., 2000), type of epilepsy and antiepileptic drugs (Oostrom, van Teeseling, Smeets-
Schouten, Peters, & Jennekens-Schinkel, 2005). Mixed results have been reported on
cognitive development over time in relation to persistence of seizures (Bjornaes, Stabell,
Henriksen, & Loyning, 2001; Jones, Siddarth, Gurbani, Shields, & Caplan, 2010;
Oostrom et al., 2005).
Limitations of the Study
The sample in the present study was available from tertiary epilepsy settings, which, by
nature, deal with more difficult to treat epilepsies and therefore possibly with children
with worse prognosis. The lower age at T1 of the children included in the sample,
compared to those not selected, also points to the inclusion of “worse” epilepsies (Bulteau
et al., 2000) and may limit the generalizability of the results.
Future Directions
Future studies with larger samples may allow insight into the possible impact of epilepsy
variables on cognitive decline. Alternatively, more specific epilepsy subsamples (for
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
60
example, specific epilepsy syndromes) could be studied with similar methods. Also, given
that epilepsy surfaces at various ages and may last for a prolonged period of time, it is
important to obtain data on reliable cognitive when different intelligence tests are used at
T1 and T2 versions (for example, a childhood version of Wechsler’s scales followed by
an adult version).
Clinical Implications
The results of the present study contribute to the literature on the cognitive course of
epilepsy in children and should be of value for clinicians and researchers. The procedure
described for the Dutch Wechsler tests can readily be applied to other languages and
cultures, provided that coefficients of stability and standard deviations are available from
a reference sample. Clinicians may want to apply the reliable change cut-off values when
retesting a child. For the American WISC-III, and for the Dutch Wechsler tests, the data
presented in Table 4.3 would yield appropriate estimates. After a change in test version
(WISC-RNL to WISC-IIINL), adjustment with the differences reported by Schittekatte (2005) is
pertinent; applying the more stringent criteria of 19 (VIQ), 18 (PIQ) and 17 (FS-IQ)
points for reliable loss is recommended to account for the predictable Flynn effect. The
present study used a statistically sound methodology to help addressing the question, “Is
this child presenting a reliable cognitive change at retesting?” The applicability of the
procedure goes beyond children with epilepsy to other neurodevelopmental disabilities
potentially associated with cognitive decline.
CHAPTER 5
Duration of epilepsy and cognitive development in children:
A longitudinal study
Loretta van Iterson
Bonne H.J. Zijlstra
Aryan van der Leij
Peter F. de Jong
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
62
Abstract
Objective: To study the pattern of cognitive development in relation to duration of
epilepsy.
Methods: Participants were 113 children with epilepsy referred because of concerns about
their cognitive development and tested at least twice at tertiary epilepsy settings. Verbal,
performance and full-scale IQ were measured with Wechsler Intelligence Scales. Various
epilepsy and demographic variables were included. Change over time was modelled with
multilevel analysis for longitudinal data with variable measurement occasion.
Results : The verbal and full scales could be fitted best as a downward progressing
function. Earlier in time, decline was likely to be largest; later in time, decline followed a
continuous, dwindling course. A similar trend was seen for the performance scale.
Initially, verbal IQ was higher than performance IQ but this discrepancy decreased over
time. Later onset of epilepsy was associated with an attenuated decline of the verbal
Scale. None of the other epilepsy variables were related to the course of cognitive
development. Higher parental education was associated with higher IQ, but was not
protective against decline.
Conclusions: verbal IQ, though initially spared, drops. The performance IQ, which may
have shown its vulnerability earlier in the course of the epilepsy, shows overall smaller
changes. It is suggested that seizures impact synergistically on an affected brain, which
leads to progressive cognitive decline. Earlier onset of epilepsy is associated with
relatively higher VIQ, larger VIQ > PIQ discrepancies and more decline.
van Iterson, L., Zijlstra, B. J., Augustijn, P. B., van der Leij, A., & de Jong, P. F. (2014). Duration of epilepsy and cognitive development in children: a longitudinal study. Neuropsychology, 28(2), 212-221.
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
63
Introduction
There is little doubt that epilepsy in childhood has an adverse impact on a child’s life
(Hermann, Jones, Jackson, & Seidenberg, 2012). Outcome is varied in terms of seizure
control (Geerts et al., 2010) and cognitive development (Berg et al., 2008). Follow-up
studies indicate that 50 to 60% of patients with epilepsy have a favourable course and
achieve seizure freedom after use of anti-epileptic drugs (AED) (Geerts et al., 2010;
Schmidt & Sillanpää, 2012). Neuropsychological studies on “uncomplicated epilepsies”
have shown a close to normal cognitive development over time in children with epilepsy
(Hermann, Seidenberg, & Jones, 2008; Jones, Siddarth, Gurbani, Shields, & Caplan,
2010). These studies relate to children with epilepsy who are not referred for
psychological assessment, who show seizure amelioration with or without medication;
and who do not have co-occurring problems like brain lesions or attention problems.
Generally, children attend regular classes, although school problems have been reported
in about half of the children with epilepsy (Reilly & Neville, 2011).
In a considerable proportion of children (~30%), however, epilepsy is not
uncomplicated, in terms of seizure control, cognitive development, or both. After 15 years
of follow-up, ~10% of the children with epilepsy never had been seizure free longer than
3 months, and an additional ~13% showed a varying course of remissions followed by
relapses (Geerts et al., 2010). Cognitive impairment has often been described in epilepsy
in children (Ellenberg, Hirtz, & Nelson, 1986; Nolan et al., 2003). In a community based
study it was shown that, 10 years after seizure onset, ~26% of children with epilepsy had
an estimated IQ below 80 (Berg et al., 2008).
This raises a number of questions: What is the developmental course of cognitive
functioning over time? Can evidence be found for cognitive decline? Is cognitive decline
associated with age at onset? Can epilepsy and demographic variables be identified which
affect cognitive development? Which area of cognitive development – verbal or
nonverbal – is likely to be affected most? Do the verbal and nonverbal domains follow
similar trajectories?
There is a body of research regarding epilepsy factors that contribute to the
severity of cognitive impairment. Apart from persistence of seizures (Bailet & Turk,
2000; Berg, Zelko, Levy, & Testa, 2012), epilepsy syndrome is recognized as an
important factor. Generalized symptomatic epilepsies are associated with low IQs (Berg
et al., 2008; Bulteau et al., 2000; Nolan et al., 2003). Localization related epilepsies and
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
64
idiopathic epilepsies are associated with relatively better outcome (Bulteau et al., 2000;
Nolan et al., 2003; Northcott et al., 2007).
Early age at onset of epilepsy (AOE) has been related to worse outcome (Cormack
et al., 2007), especially when the seizures remain active (Berg et al., 2012). Also, greater
number AEDs (Bulteau et al., 2000; Nolan et al., 2003; Smith, Elliott, & Lach, 2002),
have been associated with more cognitive impairment. Demographic factors like parental
educational level have been acknowledged as being strongly associated with children’s
IQ in children without epilepsy (Lange, Froimowitz, Bigler, Lainhart, & Brain
Development Cooperative, 2010), as well as in children with epilepsy (Mitchell, Scheier,
& Baker, 1994).
In cross-sectional studies, cognitive problems have been described both for the
verbal and nonverbal (performance) domains. Studies on specific syndromes have
reported lowered verbal IQ (Overvliet et al., 2011); studies on mixed samples have
reported lowered nonverbal IQ (Høie et al., 2005; O'Leary, Burns, & Borden, 2006;
Smith et al., 2002). Longitudinal studies focussing on the relation of the verbal and
nonverbal (i.e. performance) domains are scarce. Changes have been reported to be small
and similar for both the verbal and performance IQ (Aldenkamp, Alpherts, De Bruine-
Seeder, & Dekker, 1990; Bjornaes, Stabell, Henriksen, & Loyning, 2001). Studies on
children who underwent epilepsy surgery report increases on the performance scale,
regardless of hemispheric side of surgery (Skirrow et al., 2011).
The pattern of cognitive impairment and cognitive change over time in children
with epilepsy is still insufficiently understood. Models have been proposed describing
cognitive decline as either gradually progressive (“linear”) or, as a stepwise (“cascadic”)
decline. A cascadic decline is described as marked in the early stages of epilepsy and
plateauing thereafter (Devinsky & Tarulli, 2002; Meinardi, Aldenkamp, & Nunes, 1992;
Seidenberg, Pulsipher, & Hermann, 2007). There is still a dearth of studies to substantiate
these models on cognitive decline over time and there is still a need for a finer
characterization of the course of development in children with epilepsy (Hermann, Jones,
Jackson, & Seidenberg, 2012). This study examined the developmental trajectory of
cognitive decline in children with epilepsy. More specifically, the course of epilepsy –
without intervening epilepsy surgery – over time was considered, based on cognitive data
on children at a Dutch tertiary epilepsy center. The children were tested two or three
successive times with the Wechsler Intelligence Scales.
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
65
Various methodological problems need to be considered. These problems stem
from the heterogeneity of epilepsies in terms AOE, IQ and course (Camfield, Camfield,
Gordon, Smith, & Dooley, 1993; Schmidt & Sillanpää, 2012). Inexorably, this means a
large variability in time elapsed between epilepsy onset and time of first, second, or even
third neuropsychological testing. As children grow older, they are likely to make a
transition from one Wechsler test to another, implying variability in test versions used.
Multilevel modelling (Snijders & Bosker, 1999), a special statistical technique, was
applied to account for these differences.
Methods
Participants
From 452 Dutch children with epilepsy who had completed a Wechsler test, 113
were selected as they met the criteria for inclusion. Children were selected if they (1)
were 4 to 15 years of age at first testing (T1), (2) had been tested at least two times with
age-appropriate child Wechsler tests with an intertest interval of one year or longer, and
(3) had not had intervening epilepsy surgery in between testing. The children presented
either at a Dutch tertiary epilepsy centre or at a special school affiliated with the centre,
which provided educational support for children with epilepsy. Reasons for T1 were
concerns about the cognitive development of the child. Commonly results of the
assessment were also used in applications for special financial and educational services to
support the child within a regular or a special school. Reasons for second and third testing
(T2, T3) were requests for follow-up as the epilepsy evolved and retesting was required
for continuation of the educational support. No exclusionary criteria were set for type of
epilepsy or initial IQ. Overall, there were 249 Wechsler test measurements: 113 at T1,
113 at T2, and 23 at T3.
The sample tested 3 times (n = 23) did not differ from the sample tested 2 times (n = 90)
on any IQ scale at T1 or T2, at any epilepsy variable (AOE, duration up to T1 or T2,
epilepsy type or syndrome severity), or demographic variable (sex, handedness, parental
education).
The 339 children not selected for the study had been tested only once (n = 303);
had been tested with an age-inappropriate test (n = 3); had been retested within a year (n
= 22); had been retested with another Wechsler Scale (WAIS/WAIS-IIINL n = 5, other n = 1);
had undergone surgery (n = 2) or had missing values on an IQ scale (n = 2); other reasons
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
66
(n = 1). Comparison of selected and non-selected children (after Bonferroni adjustment)
showed similarities in terms of IQ at T1, seizure type and syndrome severity or AOE.
However, duration up to T1 and age at T1 was higher in the non-selected sample (n = 19
had been tested with the WAIS/WAIS-IIINL).
Wechsler test versions
The study concerns the scaled scores of the Wechsler Intelligence Scales in the
Netherlands, here designated as WPPSI-RNL (Vander Steene & Bos, 1997), WISC-RNL (van
Haasen et al., 1986) and WISC-IIINL (Wechsler, 2005) allowing test changes between T1
and T2 or T3.
Other measures
Epilepsy variables. The epilepsy variables were available from neurological or
neuropsychological reports and relate to information as documented at last testing. AOE,
seizure type, onset side and topographical localization, presence of MRI lesion, and
number of AEDs tried in the course of the epilepsy were included. Seizure status was
scored as active or inactive (seizure freedom of at least one year), uncertain or unknown.
The Syndrome Severity Scale for Children with Epilepsy (ESSS-C; Dunn, Buelow,
Austin, Shinnar, & Perkins, 2004) was used to measure epilepsy syndrome severity. This
10-point scale encompasses various epilepsy variables such as seizure type, aetiology and
AOE within a single scale. The present sample included syndrome severity scores ranging
from 2 to 9: idiopathic localization related epilepsy (benign epilepsy with centrotemporal
spikes [BECTS], n = 4, 3.5%, score 2); localization related symptomatic epilepsy (by
virtue of aetiology, n = 19, 16.8%, score 7; by virtue of localization n = 36, 31.9%, score
5; cryptogenic n = 5, 4.4%, score 6.55); idiopathic generalized epilepsies (childhood
absence epilepsy [CAE], n = 2, 1.8%, score 3; other n = 10, 8.8%, score 5); symptomatic
generalized epilepsies (n = 6, 5.3%, score 8-9); epilepsy syndromes (epilepsy with
continuous spikes and waves during slow sleep [CSWS] and atypical BECTS, n = 14,
12.4%, score 8; other (n = 10, 8.8%, scores 2-6); unknown (n = 7, 6.2%).
Demographic variables. The educational status of the child was dichotomized as
regular education (with special facilities) or special school placement. For parental
education, the highest educational level completed (CBS, 2007) was averaged across
parents. Sex and handedness were also included.
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
67
Analyses
The trajectories over time of verbal, performance, and full-scale IQs and VIQ – PIQ
discrepancy were estimated with a multilevel model for longitudinal data with variable
measurements occasions (Snijders & Bosker, 1999). An advantage of this model is that it
allows any number of repeated observations for each subject, without restrictions on the
temporal spacing between the measurements. Time was taken to be the duration since the
first epileptic seizure. Test versions were modelled with separate dummy variables for the
WPPSI-RNL and the WISC-IIINL, taking the WISC-RNL as reference. The predictors time and
test version could change over repeated observations, whereas the demographic and
epilepsy variables were constant for each child. For these variables the effect on
individual differences in IQ level (regardless of time), as well as the effect on individual
differences in the rate of change in IQ over time were estimated. Individual differences
unaccounted for by the predictors were modelled with a random intercept. The standard
deviation of the random intercept indicates the amount of residual differences in IQ level.
A random slope for residual differences in the rate of change in IQ over time was not
included because these models could not be estimated (the models were not identified).
The selection of an appropriate model to fit the trajectories of the IQ scores over
time was done in three steps, applying a statistical significance level of .01. Results of the
first step led to the Base Model, results of the last step to the Final Model.
In the first step, an adequate model for the change over time was sought, entering
duration of epilepsy as well as Wechsler test version. Curvilinear functions (quadratic,
logarithmic and square root) of duration of epilepsy in months (and months plus one for
the logarithmic transformation) were added to the linear model to check for a significant
increase in model fit. To find the most parsimonious model, a reverse strategy was also
applied by dropping the linear component whenever this did not significantly decrease the
model fit. Models with the same number of parameters were compared on Bayes factors
(Kass & Raftery, 1995), approximated from the Schwarz criterion, to assess the evidence
in favour of the best fitting model. This model was called the Base Model.
In the second step, time and Wechsler test version were maintained and AOE (in
months) and the demographic variables were included in the model: special education,
parental education, and the dummy variables handedness (left-handedness was coded 1),
and sex (boy was coded 1). For these predictors, the effect on the individual differences in
IQ level were always included in the model. The effect on individual differences in the
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
68
rate of change in IQ over time were only included whenever they reached statistical
significance. For AOE and parental education, the overall means were set to zero.
Table 5.1. Characteristics of the sample
N mean SD range Full sample 113 Age at onset of epilepsy (AOE) 113 4.8 3.0 0.1 to 13.2 Age at T1 113 8.4 2.3 4.7 to 15.0 Age at T2 113 11.2 2.7 5.8 to 16.9 Age at T3 23 12.9 2.7 6.9 to 16.8 Duration epilepsy to T1 113 3.5 2.6 0.2 to 12.2 Duration epilepsy to T2 113 6.3 3.1 1.6 to 15.8 Duration epilepsy to T3 23 8.6 3.5 3.3 to 16.2 AEDs tried 102 2.5 1,7 0 to 12 Epilepsy syndrome severity 106 5.9 1.6 2 to 8 Parental education 105 4.4 0.9 2.7 to 6.0 N n % Seizure type 113 Generalized seizures 15 13.3 Focal (all focal) 75 66.4 Focal: LH / RH 33 / 17 29.2 / 15.0 Bilateral of mutifocal 25 22.1 Uncertain 16 14.2 Unknown 7 6.2 MRI - / MRI + 113 82 / 31 72.6 / 27.4 Boys / girls 113 61 / 52 54 / 46 Education: regular / special 112 61 / 51 54.5 / 45.5 Test versions Test version at T1 113 WPPSI-RNL 20 17.7 WISC-RNL 63 55.8 WISC-IIINL 30 26.5 Test version at T2 113 WPPSI-RNL 3 2.7 WISC-RNL 46 40.7 WISC-IIINL 64 56.6 Test version at T3 23 20.4 WPPSI-RNL 1 0.9 WISC-RNL 5 4.4 WISC-IIINL 17 15.0
Note. T1 = test 1, T2 = test 2, T3 = test 3, AEDs tried = number of anti-epileptic drugs tried, LH = left hemisphere, RH = right hemisphere, MRI + = lesion found on neuroimaging, MRI– = no lesion found on neuroimaging or no neuroimaging available.
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
69
In the third step, the predictors from the first two steps were maintained and the epilepsy
variables were included in the model. The predictors entered were focal seizures,
generalized seizures, left hemisphere seizures, right hemisphere seizures, frontal seizures,
ESSS-C score of epilepsy syndrome severity, seizure freedom, number of AEDs tried,
MRI lesion (and its interaction with handedness). In this step, the effects of the predictors
on individual differences in IQ level and on individual differences in the rate of change in
IQ over time were included in the model only whenever they reached statistical
significance. However, for the latter effect (change over time) statistical significance had
to be established taking into account the (possibly non-significant) effect of the predictor
on individual differences in IQ level.
Table5.2 Wechsler Intelligence Scale data at different measurement points
Full sample Subsample Scale T1 T2 T1 T2 T3 Mean SD Mean SD Mean SD Mean SD Mean SD VIQ 89.3 15.4 81.5 15.9 85.7 11.8 73.1 13.0 71.4 11.9 PIQ 84.3 17.0 82.1 18.0 80.3 15.4 76.5 15.9 72.3 16.2 FS-IQ 85.5 16.0 79.8 16.9 81.5 12.8 73.7 11.7 69.3 13.0 VIQ - PIQ 5.0 14.1 -0.6 14.1 5.7 14.8 -0.3 18.4 -0.9 14.0
Note. The Full sample was based on n = 113. The subsample included the 23 children who had been adminstered the Wechsler three times.
Results
Table 5.1 shows the characteristics of the sample and Table 5.2 the unadjusted mean IQs.
Results for the longitudinal multilevel models are presented in Table 5.3 (for the verbal
scale, performance scale, and full scale) and Table 5.4 (VIQ – PIQ discrepancy) and
include the Base and Final Model. None of the epilepsy predictors could be added to the
final models in the third stage of model selection. Therefore, the Final Model comprises
the results of the second stage.
Figure 2.1 shows the approximated predicted outcomes according to the Base Model
for the verbal, performance and full-scale IQs for the middle 95 percent of the observed
durations of epilepsy (i.e. ranging from 8 to 146 months). The figure shows a strong
decline initially, leveling off with increasing duration of the epilepsy. Bayes factors for
the performance scale and full scale indicated positive to very strong evidence (Kass &,
C
OG
NIT
IVE
PATT
ERN
S IN
PA
EDIA
TRIC
EPI
LEPS
Y
Ta
ble
5.3.
Res
ults
of m
ultil
evel
ana
lysi
s for
the
Bas
e M
odel
(Mod
el) 1
and
the
Fina
l Mod
el.
Ver
bal S
cale
Pe
rfor
man
ce S
cale
Fu
ll Sc
ale
Estim
ate
S.E.
p-
valu
e 95
% C
I Es
timat
e S.
E.
p-va
lue
95%
CI
Estim
ate
S.E.
p-
valu
e 95
%C
I
Bas
e M
odel
Fixe
d ef
fect
s Inte
rcep
t 11
5.57
3.
86
<.00
1 (1
08.0
, 123
.2)
103.
16
4.50
<
.001
(9
4.3,
112
.0)
109.
35
3.98
<
.001
(1
01.5
,117
.2)
Tim
e: lo
garit
hmic
-7
.56
0.97
<.
001
(-9.
5, -5
.6)
-4.9
7 1.
14
< .0
01
(-7.
2, -2
.7)
-6.6
6 1.
00
< .0
01
(-8.
6, -4
.7)
WIS
C-II
INL
-3.0
4 1.
72
.078
(-
6.4
, 0.3
) -1
.93
2.01
.3
37
(- 5
.8, 2
.0)
-2.9
0 1.
77
.104
(-
6.4
, 0.6
)
WPP
SI-R
NL
-1.1
7 2.
51
.641
(-
6.1,
3.7
) -2
.99
2.98
.3
17
(-8.
9, 2
.9)
-1.7
5 2.
60
.502
(-
6.9,
3.4
)
Ran
dom
eff
ects
In
terc
ept S
D
13.2
9 13
.85
13
.54
R
esid
ual S
D
7.53
9.
19
7.
82
Dev
ianc
e 19
44.0
0
20
12.7
9
19
58.9
6
70
DU
RA
TIO
N O
F EP
ILEP
SY A
ND
CO
GN
ITIV
E D
EVEL
OPM
ENT
Ta
ble
5.3.
Res
ults
of m
ultil
evel
ana
lysi
s for
the
Bas
e M
odel
(Mod
el) 1
and
the
Fina
l Mod
el (c
ontin
ued)
.
V
erba
l Sca
le
Perf
orm
ance
Sca
le
Full
Scal
e
Estim
ate
S.E.
p-
valu
e 95
% C
I Es
timat
e S.
E.
p-va
lue
95%
CI
Estim
ate
S.E.
p-
valu
e 95
%C
I
Fina
l Mod
el
Fixe
d ef
fect
s Inte
rcep
t 12
4.71
5.
58
< .0
01
(113
.7, 1
35.7
) 98
.24
5.77
<
.001
(8
6.9,
109.
6)
106.
75
4.77
<
.001
(9
7.3,
116
.1)
Tim
e: lo
garit
hmic
-9
.34
1.30
<
.001
(-
11.9
, -6.
8)
-3.2
8 1.
37
.018
(-
6.0,
-0.6
) -5
.62
1.12
<
.001
(-
7.8,
-3.4
)
WIS
C-II
INL
-2.1
3 1.
66
.200
(-
5.4,
1.1
) -2
.20
2.16
.3
11
(-6.
4, 2
.1)
-2.4
9 1.
80
.170
(-
6.0,
1.1
)
WPP
SI-R
NL
-5.0
9 2.
65
.056
(-
10.3
, 0.1
) -1
.38
3.28
.6
74
(-7.
8, 5
.1)
-1.6
8 2.
69
.534
(-
7.0,
3.6
)
Age
at o
nset
-0
.33
0.12
.0
07
(-0.
6, -0
.9)
0.15
0.
05
.001
(0
.1, 0
.2)
0.08
0.
04
.059
(-
0.00
3, 0
.2)
Pare
ntal
edu
catio
n 6.
54
1.36
<
.001
(3
.8, 9
.2)
4.25
1.
63
.011
(1
.0, 7
.5)
5.79
1.
42
< .0
01
(3.0
, 8.6
)
Le
fthan
dedn
ess
-3.5
8 2.
95
.228
(-
9.4,
2.3
)
-4.7
9 3.
52
.178
(-
11.8
, 2.2
)
-4.2
4 3.
07
.170
(-
10.3
,1.9
)
Sex
(boy
) 5.
96
2.34
.0
12
(1.3
, 10.
6)
2.56
2.
80
.362
(-
3.0,
8.1
) 4.
46
2.44
.0
70
(-0.
4, 9
.3)
Spec
ial e
duca
tion
-9.1
8 2.
49
<.00
1 (-
14.1
, 4.2
) -6
.08
2.98
.0
44
(-12
.0, -
0.2)
-8
.56
2.60
.0
01
(-13
.7, -
3.4)
Ti
me:
loga
rithm
ic b
y A
OE
0.08
0.
03
.005
(0
.02,
0.1
)
R
ando
m e
ffec
ts
Inte
rcep
t SD
9.
66
11.2
7 10
.04
Res
idua
l SD
6.
87
9.52
7.
70
D
evia
nce
152
0.60
1
627.
54
155
4.47
Not
e. S
.E. =
Sta
ndar
d Er
ror.
SD =
stan
dard
dev
iatio
n
71
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
72
Table 5.4. Results of multilevel analysis for the VIQ – PIQ discrepancy.
VIQ - PIQ Estimate S.E. p-value 95%CI
Base Model
Fixed effects Intercept 9.06 2.63 .001 (3.9, 14.2)
Time: square root -0.93 0.33 .005 (-1.6, -0.3)
WISC-IIINL -0.63 1.80 .726 (-4.2, 2.9)
WPPSI-RNL 1.49 2.61 .570 (-3.7, 6.6) Random effects Intercept s.d. 11.84 Residual s.d. 8.11 Deviance 1944.38 Final Model Fixed effects Intercept 12.87 3.61 < .001 (5.7, 20.0)
Time: square root -1.53 0.40 < .001 (-2.3, -0.7)
WISC-IIINL 0.64 1.95 .745 (-3.2, 4.5)
WPPSI-RNL -1.64 2.82 .562 (-7.2, 3.9)
Age at onset -0.17 0.04 < .001 (-0.3, -0.1)
Parental education 1.85 1.52 .227 (-1.2, 4.9)
Lefthandedness 1.64 3.30 .620 (-4.9, 8.1)
Sex (boy) 2.99 2.61 .255 (-2.2, 8.2)
Special education -3.55 2.79 .206 (-9.1, 2.0) Random effects Intercept s.d. 10.85 Residual s.d. 8.01 Deviance 1576.42
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
73
Raftery, 1995) for a logarithmic decline compared to the square, linear and square root
decline. For the verbal scale there was nearly as much evidence for square root decline,
indicating that the decline leveled off to a slightly lesser degree, but there was strong
evidence against linear and square decline.
In the Final Model, results were overall similar for the verbal and full scales. The
magnitude of estimates of the effect of time was comparable between the models for VIQ
and FS-IQ. The positive effect for parental education in Table 5.3 suggested that higher
parental education was associated with higher VIQ and FS-IQ. No effect of parental
education on individual differences in change over time was found, suggesting that lower
parental education did not imply an increased risk of decline over time. Similarly, special
education was associated with lower VIQ and FS-IQ, while no significant effect for
change over time was seen. For the VIQ, an effect was seen for AOE, also in interaction
with time. The (negative) values on time and AOE indicated that a longer duration and a
later onset were associated with a lower VIQ. The (positive) value for the interaction of
AOE and time showed that the decline of the VIQ over time was somewhat less
pronounced for children with a higher AOE. No other epilepsy variable made a
significant contribution to the models.
For the performance scale, a similar albeit non-significant effect of time could be
found in the Final Model, compared to the Base Model. A positive effect could be
observed for AOE (Table 5.3), meaning that children with later AOE (above the mean of
the sample) were likely to have a slightly higher PIQ score. A positive effect was seen for
parental education also.
The intercept for VIQ – PIQ differed from zero (Table 5.4); the positive value
indicated that the difference favoured the verbal scale (VIQ > PIQ). Change over time
presented as a square root curve, although Bayes factors for the Base Model suggested
that there was almost as much evidence for a linear or logarithmic downward slope. The
negative value for time suggested that the VIQ – PIQ discrepancies decreased over time.
The negative value for age of onset implied that a younger AOE was associated with
larger VIQ > PIQ discrepancies.
For all models, the standard deviations of the random intercepts were larger than
the residual standard deviations, implying that the differences not accounted for by the
predictors in the models between the children were larger than the residuals around the
predicted individual trajectories. Therefore, the model estimates indicated there was less
uncertainty about the individual trajectories (and their shape) than about the IQ levels of
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
74
children (regardless of time). No significant effect of Wechsler test version was seen on
any scale. Large differences between individual trajectories can be found.
Figure 5.1. Approximated effect of time according to the Base Model for Verbal IQ, Performance IQ, and Full Scale IQ.
Discussion
This study provided evidence for progressive cognitive decline over time in clinically
referred children with epilepsy. Decline was largest in the early stages of epilepsy;
thereafter, decline continued at an increasingly slower pace. Also, a differential trajectory
for VIQ and PIQ was seen, which was not described earlier. The curve described was
logarithmic – not linear. On an individual bases, cascadic decline cannot be excluded.
Large individual variation was found.
Different trajectories for the Verbal and Performance IQ
Previous studies have described lower IQ for very early AOE (Bulteau et al.,
2000; Cormack et al., 2007). The present study points towards differential impact on the
various scales: earlier AOE was associated with a better (initial) VIQ and a more
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
75
pronounced decline – therefore a worse trajectory. Earlier AOE was associated with
particularly lowered PIQ and larger VIQ > PIQ discrepancies.
The VIQ > PIQ gap was seen early in the course of the epilepsy, and closed over
time. The results suggested that verbal IQ was “spared” initially and declined over time,
while performance IQ possibly showed its vulnerability early in the course of the epilepsy
and showed an attenuated decline later on. Future research including children tested
before epilepsy onset (for example, children with an increased genetic risk for developing
epilepsy), could be directed at elucidating whether the VIQ > PIQ discrepancy exists
already prior to the onset of epilepsy, or emerges together with the seizure condition.
As in the present study, some evidence for less decline (or more gains) at retesting
for PIQ rather than VIQ has been given in samples without intervening surgery
(Aldenkamp, Alpherts, De Bruine-Seeder, & Dekker, 1990), after epilepsy surgery
(Skirrow et al., 2011; Westerveld et al., 2000), and in the light of amelioration of seizures
(van Mil et al., 2010). Part of these effects may be interpreted in the light of studies of
children without epilepsy, where the performance scale has been shown to be more prone
to profit from practice effects or test familiarity (Canivez & Watkins, 1998). The impact
of test familiarity in the present study should be limited, given the interval of a year or
longer between testings.
Variables contributing to cognitive level and cognitive change
Epilepsy variables.
Similar to earlier studies on heterogeneous samples (Reijs et al., 2007; Strauss et
al., 1995), no epilepsy variable other than age of epilepsy onset and duration of epilepsy,
could be singled out as contributing significantly to cognitive level or to cognitive change
over time. This is particularly puzzling concerning variables like epilepsy syndrome
severity and underlying symptomatology. Several issues should be pointed out regarding
the variables studied. First, the results may challenge the utility of the syndrome severity
scale as used in this study. In fact, various authors have indicated that the best way to
determine epilepsy syndrome severity is still under debate and that syndrome severity
should be studied in combination with cognitive outcome (Dunn et al., 2004; Reijs et al.,
2006; Wirrell, Grossardt, So, & Nickels, 2011). Second, as Elger, Helmsteadter and
Kurthen (2004) pointed out, aetiology and AOE are difficult to disentangle, because
specific disorders peak at certain age groups (Wirrell, Grossardt, Wong-Kisiel, & Nickels,
2011). This means that the findings related to early AOE may be seen as valid for early
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
76
onset aetiologies. Third, some cases with MRI-negative findings are reclassified as
positive cases after MRI reevaluation (Funke, Moore, Orrison, & Lewine, 2011). Changes
in MRI-interpretation have implications for the reliability of the distinction between
symptomatic and nonsymptomatic aetiologies and consequent syndrome classification.
Fourth, seizure freedom may be temporary and may be followed by relapse (Schmidt &
Sillanpää, 2012). Fifth, AEDs can both impair and enhance cognitive functioning (Kwan
& Brodie, 2001). All these issues may be of particular relevance in long-term evaluations
of children and may aid in explaining why none of these variables had a statistically
significant contribution to the models.
Newer types of seizure classification and conceptualization are being proposed
(Berg et al., 2010). These classifications may potentially prove to be differentially
associated with cognitive outcome in epilepsy. Literature suggests that an underlying
cause leading to seizures – be it hereditary, structural, metabolic, or unknown (Berg et al.,
2010) – may affect the cognitive development of the child even before the epilepsy
surfaces (Schouten, Oostrom, Jennekens-Schinkel, & Peters, 2001), and may continue to
influence cognitive development for a prolonged period of time.
The present study proposes that the impact of the seizure condition on an already
affected brain (Hermann et al., 2006) is synergistic, leading to progressive decline in
cognitive function. The impact of selected epilepsy variables on this decline could not
easily be singled out. Evidence of this synergistic effect is also provided by studies on
unilateral brain lesions, showing that the added presence of seizures alters the course of
cognitive development, turning growth into decline (Ballantyne, Spilkin, Hesselink, &
Trauner, 2008). Further support comes from recent studies showing changes in brain
networks of children with focal epilepsy, which extend beyond the epileptic region and
was most prominent in children with lower IQ (Braakman et al., 2012). A study on adults
showed that changes in brain networks could be associated with cognitive decline
(Vaessen et al., 2012). The decisive factor leading to cognitive decline may not be the
presence or absence of seizures or a brain lesion alone, but their co-occurrence and
possible interaction.
Demographic variables, special education.
School problems are frequently seen in children with epilepsy (Fastenau, Shen,
Dunn, & Austin, 2008; Reilly & Neville, 2011). In the present study, special education
was associated with lower VIQ. No interaction with the duration of epilepsy was seen,
suggesting that special education per se is not associated with lowering of IQ over time.
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
77
In line with research showing that parental education is a predictor of cognitive
functioning (Mitchell, Scheier, & Baker, 1994), a significant effect was seen for parental
educational level. Again, no interaction was seen between parental education and change
over time, suggesting that lower parental education was no risk factor for decline.
Conversely, higher parental education was not “protective” against decline.
Test versions.
Epilepsies emerge at different ages and progress with remissions and relapses
(Camfield, Camfield, Gordon, Smith, & Dooley, 1993; Schmidt & Sillanpää, 2012). A
first step to approaching a wide spectrum of epilepsies longitudinally is the inclusion of
children at various ages – and therefore the inclusion of various test versions, and changes
of test versions over time. However, studies with more than one test version carry the risk
of contaminating results with non-equivalence of test versions and Flynn effects
(Bourgeois, Prensky, Palkes, Talent, & Busch, 1983; Flynn, 2007; Kaufman, 2010;
Neyens & Aldenkamp, 1996). A major advantage of the present study is that it modeled
different test versions explicitly, adjusting for their potential differential contributions.
Similarly, the present model allowed entering children regardless of the number of times
they were tested.
Mechanisms related to the initial drop in IQ and the posterior slowing of decline
The mechanisms leading to an initial drop – differential for the two IQ scales and more
pronounced in the younger child – and posterior stabilization of cognitive functions are
not completely understood. The large individual variability among individuals suggests
different mechanisms between individuals and between aetiological groups. An approach
to understanding the mechanisms may be undertaken from the perspective of the
interaction between brain, epilepsy and cognitive function; and psychometrics.
Before the onset of epilepsy, learning problems may already be seen
(Hermann, Jones, Jackson, & Seidenberg, 2012; Schouten, Oostrom, Jennekens-Schinkel,
& Peters, 2001) pointing towards ongoing latent changes in the brain (Hermann et
al., 2010). The period of epileptogenesis culminates in the disruption of the balance
between excitation and inhibition of the brain network (Jensen, 2011), and in the
emergence of seizures proper. Generalized, non-specific cognitive problems become
evident, affecting mainly attention, executive functions and visual-motor speed (Bhise,
Burack, & Mandelbaum, 2009; Fastenau et al., 2009; Hermann et al., 2006;
Hermann et al., 2012). These difficulties may give rise to the lowered PIQ and to the
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
78
concomitant VIQ > PIQ gap described in the present paper. Soon after the seizures
become apparent, abnormalities in brain organization are seen. Of particular interest in
relation to the low PIQ, are the white matter abnormalities and a disturbed pattern of
white matter growth observed in several studies (B. P. Hermann et al., 2010; Hutchinson
et al., 2010) which may hamper speed of information processing.
The more preserved VIQ at first testing may be giving a closer estimate of the
child’s original cognitive level. PIQ, with its lower initial score and more gradual decline,
may be giving a better indication of the vulnerable reaction of the brain already during
this process of epileptogenesis and emergence of the seizure condition. The younger
child, with its more immature brain, has a reduced seizure threshold and is particularly
vulnerable to disruption (Rakhade & Jensen, 2009) and more prone to show impaired
cognitive development (Cormack et al., 2007), a lowered PIQ and a worse trajectory of
VIQ.
With the emergence of seizures, the already ongoing process of abnormal
development exacerbates, leading to a cascade of changes, both in the brain and in
cognition (Jensen, 2011; Rakhade & Jensen, 2009). Cognitive decline becomes more
generalized and affects also the initially spared verbal IQ. Information that was already
acquired and consolidated (“wired”) may be preserved and account for the initial higher
level of VIQ. In order to maintain the original IQ, children must earn higher raw scores
when they grow older (Wechsler, 2005). An adverse impact of the epilepsy on novel
problem solving abilities and on the ability to acquire new information, may account for
the reduced rate of cognitive growth. Reduced cognitive growth is detected by the IQ test
as lower scores at retesting. Decline in verbal IQ shows a steeper downward curve and
becomes more evident over months or years. The present data suggest that this decline in
verbal IQ can possibly be understood as being largely non-specific (associated to the
failure of the brain to develop and to acquire new information in the same pace as before)
rather than specific (associated with the brain areas responsible for language and verbal
abilities). A non-specific effect is suggested by the lack of association between side of
seizure onset (right versus left hemisphere) with VIQ – PIQ difference or with the VIQ –
PIQ pattern (the closing of the VIQ – PIQ gap) over time. Also, the association between
low verbal IQ and the presence of epileptic activity during the night described in the
literature (Overvliet et al., 2011) may be interpreted as largely non-specific.
Over time, the brain itself may activate (inhibitory) mechanisms to deal with the
heightened excitation of the brain, possibly due to brain maturation (Rakhade & Jensen,
DURATION OF EPILEPSY AND COGNITIVE DEVELOPMENT
79
2009), leading to self-containment of the seizure condition. In addition, anti-epileptic
medications aid in seizure suppression and affect cognitive function (Geerts et al., 2010;
Kwan & Brodie, 2001). Many of the childhood epilepsies ameliorate after several years
(Geerts et al., 2010). These factors may all contribute to slowing down cognitive decline,
giving way to renewed development, although often at a lower level than the original
level. The role of reorganization of brain networks, and its implications for cognitive
development remains unclear. Alterations in brain networks in children with frontal lobe
epilepsy were seen more clearly in those with low intellectual ability but were not
associated with duration of epilepsy (Braakman et al., 2012). The timing of the initial
epileptic seizure, the effects of AEDs and changes in brain development may depend on
aetiology and may be positive in some children and negative in others, explaining the
difference between those who continue to decline and those who resume development.
Further research is needed to determine the factors. In some children with low IQs
already at baseline, reaching the floor of the test may occur; thereafter, decline can no
longer be quantified by the test.
Limitations and utility of the study
An important consideration is that this study used a clinical sample from a tertiary
epilepsy setting to study the cognitive course over time. The children had been referred
and repeatedly assessed because concerns about the neuropsychological functioning had
risen. The sample consisted of children who were more likely to have refractory epilepsy,
epilepsy with an unstable course (Geerts et al., 2010; Schmidt & Sillanpää, 2012),
epilepsy that changed into a more atypical and severe forms (Fejerman, Caraballo, &
Tenembaum, 2000), and relatively higher rates of children with moderate and high
epilepsy severity. Therefore, the results cannot be generalized to “uncomplicated”
epilepsy without developmental concerns. However, it should be borne in mind that
cognitive problems are not restricted to children with more severe epilepsies or epileptic
encephalopathies.
Just as uncomplicated cases may not be referred for testing, it would be expected
that children tested more often (one versus two, two versus three times) may be “worse”
cases. Preliminary analyses showed that – up to second testing – these differences were
not statistically significant.
Given the differential impact of duration of epilepsy on the VIQ and PIQ, (cross-
sectional) studies should always include data on duration of epilepsy.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
80
Clinical implications
Vulnerability at group level implies that clinically significant decline is likely to
be found for a proportion of the children with epilepsy. An earlier study on reliable
cognitive change suggested that this proportion is indeed elevated (van Iterson, Augustijn,
de Jong, & van der Leij, 2013).
Lowering of IQ is associated with the failure of school children to progress in
school. Repeating grades, being set down to a lower type of education than they enrolled
at the start of secondary school, or both, were seen in a substantial 70% of a sample of 32
youngsters referred to a tertiary centre. A correlation of .41 was found between failure to
maintain school level and reliable cognitive decline (van Iterson, 2010). Even after IQs
cease to drop, the result is a significantly reduced school career – with its implications for
emotional adjustment and future perspectives.
Clinicians, parents and educators should stay alert to signs suggesting that a child
is deviating from its developmental curve, particularly early in the course of the epilepsy,
but also beyond the first years. This deviation may be observed as failure to grow in the
pace expected for the age, or worse, as loss of acquired cognitive abilities. The risk of
decline should also be considered in epilepsies deemed of lower severity.
The study advocates for the inclusion of cognitive outcome in measures of
severity. That is, a child with epilepsy who shows concomitant slowing or stagnation of
development or loss of cognitive functions, should be reclassified adding a constituent on
cognitive function in the descriptive diagnosis, regardless of whether the initial epilepsy
diagnosis per se is of low or moderate severity and whether diagnosis has changed after
re-evaluation. This would be in agreement with the revised guidelines of the ILAE stating
that: “encephalopathic effects [i.e. severe cognitive impairment] of epilepsy may occur in
association with any form of epilepsy” (Berg et al., 2010, p.682). This addition on
cognitive course should have implications for the educational needs of the child.
In like manner, the study advocates an early start of long-term remediation
interventions for all children who develop epilepsy. This educational remediation should
be an individually-tailored process, if necessary lasting beyond seizure remission. Also,
the results of the present study urge researchers to intensify the search for underlying
aetiologies and optimization of medical treatment.
CHAPTER 6
Paediatric Epilepsy and Comorbid Reading Disorders, Math Disorders or
Autism Spectrum Disorders: Impact of Epilepsy on Cognitive Patterns
Loretta van Iterson
Peter F. de Jong
Bonne J.H. Zijlstra
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
82
Abstract
Introduction: In paediatric epilepsy, comorbidities are reported to be frequent. The
present study focussed at the cognitive patterns of children with isolated epilepsy,
children with isolated neurodevelopmental disorders (reading disorders, math disorders,
autism spectrum disorders) and children with epilepsy and these neurodevelopmental
disorders as comorbidities.
Methods: based on two samples of referred children, one with epilepsy, reading disorders,
math disorders or ASD occurring in isolation (n = 117), and one with reading disorders,
math disorders and ASD occurring comorbid with epilepsy (n = 171), cognitive patterns
were compared. The patterns displayed by verbal and nonverbal abilities from the WISC
series were studied with repeated measures ANOVA. Thereafter, an exploratory 2*3*2
factorial analysis was done to study the independent contribution of type of comorbidity
and of presence or absence of epilepsy on the VIQ – PIQ pattern.
Results: In isolated epilepsy, a VIQ > PIQ pattern was found, not seen in the other
disorders. When epilepsy and another disorder co-occurred, patterns were altered. They
resembled partly the pattern seen in isolated epilepsy and partly the pattern seen in the
isolated neurodevelopmental disorder. In comorbid reading disorders, the VIQ > PIQ was
mitigated; in comorbid math disorders, it was exacerbated. In comorbid ASD, no clear
pattern emerged. In the presence of epilepsy, patterns characteristic of isolated disorders
appear systematically shifted towards relatively lowered performance abilities or
relatively spared verbal abilities. The similar “impact” exerted by the epilepsy on the
patterns of the various conditions suggested shared mechanisms.
van Iterson, L., De Jong, P. F., & Zijlstra, B. J. (2015). Pediatric epilepsy and comorbid reading disorders, math disorders, or autism spectrum disorders: Impact of epilepsy on cognitive patterns. Epilepsy and Behavior, 44, 159-168.
PATTERNS IN COMORBIDITIES
83
Introduction
Seizure conditions in children are heterogeneous disorders in terms of age at onset,
severity, type of seizures, response to medication, duration and cognitive outcomes (Berg
et al., 2008; Schmidt & Sillanpää, 2012). They are often accompanied by general
cognitive problems as a somewhat lowered IQ (Berg et al., 2008; Elger, Helmstaedter, &
Kurthen, 2004; Ellenberg, Hirtz, & Nelson, 1986; Hermann et al., 2008; Nolan et al.,
2003). Besides this general impact on cognition, studies have also suggested differential
effects on cognitive patterns. A number of studies on the Wechsler Intelligence Scales for
Children (WISC series) in mixed samples of children with epilepsy referred for
neuropsychological evaluation suggest that verbal abilities (Verbal IQ, VIQ or the factor
Verbal Comprehension Index, VCI) are relatively spared, while the performance abilities
(Performance IQ, PIQ or the factor Perceptual Organization, POI) are lowered. This
differential “impact” of epilepsy on the verbal and performance scales, suggesting a VIQ
> PIQ pattern seems independent of epilepsy variables such as side of seizure onset,
seizure type, number of anti-epileptic drugs (AEDs), or presence of MRI-abnormalities
(van Iterson & Augustijn, 2006; van Iterson, Zijlstra, Augustijn, van der Leij, & de Jong,
2014). In addition, while level of IQ was lower in children in special education as well as
in children with parents with lower education, the pattern displayed by VIQ and PIQ was
not related to type of education or to level of parental education (van Iterson et al., 2014).
Neuropsychological studies on epilepsy generally include data on verbal and performance
abilities as descriptives of the participants, even when VIQ – PIQ patterns are not the
focus of the study. Based on this information, the VIQ > PIQ pattern (or, similarly, a VCI
> POI pattern) is also observed in children with epilepsy in association with mixed
samples, frontal lobe epilepsies, Panayiotopoulos syndrome, benign epilepsy with centro-
temporal spikes (BECTS) and daytime seizures, the use of polytherapy, and interictal
discharges (Lopes, Simoes, & Leal, 2014; O'Leary, Burns, & Borden, 2006; Overvliet et
al., 2011; Smith, Elliott, & Lach, 2002; Tedrus, Fonseca, Melo, & Ximenes, 2009).
However, in other studies, the opposite pattern is observed. Specifically, VIQ < PIQ
patterns have been reported in mixed samples with learning problems and in association
with BECTS and night time seizures and older age at testing (Aldenkamp, Alpherts, De
Bruine-Seeder, & Dekker, 1990; Miranda & Smith, 2001; Northcott et al., 2007;
Overvliet et al., 2011; Vago, Bulgheroni, Franceschetti, Usilla, & Riva, 2008; Verrotti et
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
84
al., 2011). Also, some studies have presented data suggestive of similar VIQ and PIQ,
such as studies on lateralized seizures, studies on mixed samples, as well as studies on
samples with epilepsy which were either referred or not referred for psychological
evaluation (Braakman et al., 2012; Jones, Siddarth, Gurbani, Shields, & Caplan, 2010;
Northcott et al., 2007; Tedrus et al., 2009; Vago et al., 2008; van Mil et al., 2009;
Westerveld et al., 2000). Overall, although there is evidence of VIQ > PIQ patterns in
epilepsy, results across studies vary, even within a single epilepsy syndrome (as in
BECTS). These inconsistencies in the literature may be associated with differences across
samples in terms of duration of epilepsy: the VIQ > PIQ pattern is mostly seen in the
early stages of the epilepsy (van Iterson et al., 2014). These differences, however, could
also be related to differences associated with comorbidities in epilepsy.
The plea to study comorbidities in epilepsy is sounding increasingly louder
(Asato, Caplan, & Hermann, 2014; Helmstaedter et al., 2014). Studies have highlighted
the relevance of comorbidities in epilepsy indicating their high frequency of occurrence
(Berg, Caplan, & Hesdorffer, 2011; Fastenau, Shen, Dunn, & Austin, 2008; Russ, Larson,
& Halfon, 2012). In particular, learning, psychiatric, social or behavioural comorbidities
have been frequently reported in children with seizures (Austin & Fastenau, 2010;
Brooks-Kayal et al., 2013; Lin, Mula, & Hermann, 2012; Russ et al., 2012). Learning
problems are common (Austin, Huberty, Huster, & Dunn, 1999; Reilly & Neville, 2011;
Russ et al., 2012) and in an epidemiological study, Russ et al. (2012) indicated that the
adjusted relative risk ratio for various kinds of school problems in epilepsy was 6.7. The
rate of children with epilepsy with reading scores below the seventh percentile has been
reported to be 20.1%; specific reading problems (i.e., based on IQ- achievement
discrepancy) comorbid with epilepsy have been reported in 12.8% of children (Fastenau
et al., 2008). For math problems, the percentage of children with epilepsy scoring below
the seventh percentile was found to be 26.8%; and 20.1% had specific math problems
based on the IQ-achievement discrepancy (Fastenau et al., 2008). Autism spectrum
disorders (ASD) are also a major comorbidity in epilepsy. Russ et al. (2012) reported a
relative risk ratio of 15.5. Rates of co-occurrence of epilepsy and autism tend to vary
from 15% (Russ et al., 2012) to 30% (Tuchman, Alessandri, & Cuccaro, 2010). ASD in
epilepsy is most often seen in the presence of intellectual disabilities; it remains unsettled
whether rates of ASD are elevated in children with epilepsy with average IQs (Berg &
Plioplys, 2012). Importantly, although some comorbidities have been reported to occur
mostly in association with specific epileptic syndromes (Besag, 2009; Clarke et al., 2007),
PATTERNS IN COMORBIDITIES
85
overall, comorbidities have been found to occur across epilepsy syndromes (Berg et al.,
2011; Fastenau et al., 2008; Lin et al., 2012).
Children with epilepsy present with neuropsychological disorders of all kinds
(Braakman et al., 2012; Hoie, Mykletun, Waaler, Skeidsvoll, & Sommerfelt, 2006;
Northcott et al., 2007). These disorders, however, need not lead to the diagnosis of
comorbidities. The disorders may be considered the neuropsychological counterpart of
the epileptic condition reflecting the interference of the seizure condition with
performance on cognitive tasks, not necessarily clustering into a specific second
diagnosis. Such children will be referred to in the present paper as children with
“isolated” epilepsy. Some authors suggest that the focus on the medical condition
(epilepsy) and its treatment may be leading to under diagnosis and underreporting of the
comorbidity (Helmstaedter et al., 2014; Matsuo, Maeda, Sasaki, Ishii, & Hamasaki,
2010). Available studies have suggested that the combined presence of epilepsy and
learning or behavioural disorders are associated with overall lowered IQ (Hermann et al.,
2008). Not much is known as to whether the neurocognitive pattern (like the pattern
displayed by the verbal and performance abilities) seen in children with epilepsy and a
second diagnosis (a comorbidity) resembles the pattern seen in the neurodevelopmental
diagnosis when it occurs as a single diagnosis without epilepsy, that is, when it occurs as
an “isolated” condition.
Henceforth, the term “isolated” will also be used to denote children with a single
diagnosis of a developmental disorder (reading, math, ASD, or epilepsy), in contrast to
the child with a comorbidity. Similar to epilepsy, children with other developmental
disorders may also have other neuropsychological weaknesses which do not qualify for a
second diagnosis. Both isolated epilepsy as well as other neurodevelopmental conditions
occurring in isolation may be characterized by patterns of cognitive strengths and
weaknesses. As said, although the results of the literature remain inconclusive, for mixed
samples of children with epilepsy referred for neuropsychological evaluation, a VIQ >
PIQ pattern of has been found. For language based neurodevelopmental disorders, like
reading and spelling disorders, patterns of relative spared performance abilities and
relatively depressed verbal abilities have been found. Pelletier, Ahmand, and Rourke
(2001) reported that 61% to 78% of their samples with reading disabilities showed a VIQ
< PIQ discrepancy of at least 10 points. For children with math problems, large
discrepancies were seen favouring either the verbal or performance scale (Desoete, 2008),
but sometimes predominantly the verbal scale (Pelletier et al., 2001). In ASD, high rates
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
86
of children (41% – 50%) have been reported to have VIQ – PIQ discrepancies of 12 or
more IQ points in either direction (Black, Wallace, Sokoloff, & Kenworthy, 2009;
Charman, Pickles, Chandler, Loucas, & Baird, 2011). Relatively high scores on the
performance scale and strengths on specific performance subtests have been found in
mixed ASD samples (Charman et al., 2011; de Bruin, Verheij, & Ferdinand, 2006;
Scheirs & Timmers, 2009), and relatively high scores on the verbal scale have been
observed particularly in Asperger syndrome (Cederlund, 2004; de Bruin et al., 2006).
Thus, in ASD both verbal strengths and performance strengths can be seen, possibly with
a predominance for a VIQ < PIQ pattern.
It has been suggested that the manifestations of neurodevelopmental disorders in
epilepsy (comorbidities) may have both commonalities as well as differences to their
manifestation as isolated conditions (Lin et al., 2012). As in isolated reading disorders,
reading problems comorbid with epilepsy have been associated with lower verbal abilities
and difficulties with verbal memory and learning (Dunn et al., 2010; Vermeulen,
Kortstee, Alpherts, & Aldenkamp, 1994). The epilepsy syndrome most consistently
associated with reading disorders is BECTS (Clarke et al., 2007). Studies on BECTS have
provided some evidence for lowered verbal abilities, but these results have been reported
as being associated with older age and the presence of night time seizures (Northcott et
al., 2007; Overvliet et al., 2011; Tedrus et al., 2009; Vago et al., 2008; Verrotti et al.,
2011). For math disorders in epilepsy, no specific patterns have been described. Problems
with processing speed, younger age of epilepsy onset, symptomatic epilepsies,
generalized seizures and frequent interictal discharges have been identified as risk factors
for math disorders (Dunn et al., 2010; Fastenau et al., 2008; Nicolai et al., 2012; Rathouz
et al., 2014). Given that both PIQ weaknesses (van Iterson et al., 2014) and math
problems (Masur et al., 2013; Rathouz et al., 2014) have been reported early in the course
of the epilepsy, a VIQ > PIQ pattern would be more likely to be seen in math problems in
epilepsy than a VIQ < PIQ pattern. For ASD and epilepsy, associations between language
disorders have been described (Tuchman et al., 2010), but literature on patterns of verbal
and nonverbal abilities in epilepsy and ASD is still scarce. Some features of a disorder
may be masked and others may be emphasized in the light of epilepsy (Lin et al., 2012),
and more work has to be done to understand cognitive patterns seen in children with
epilepsy with or without a comorbid condition.
One aim of the current study was to compare cognitive patterns of children across
conditions, both isolated conditions (that is, without an additional comorbid diagnosis) as
PATTERNS IN COMORBIDITIES
87
well as conditions comorbid with epilepsy. Two main research questions were addressed.
The first research question focussed on the pattern of verbal and nonverbal abilities in
children with isolated epilepsy contrasted (a) to control children, and (b) to children with
other isolated neurodevelopmental disorders, in particular reading disorders, math
disorders and autism spectrum disorders. The first hypothesis was that children with
isolated epilepsy would show a VIQ > PIQ (or VCI > POI) pattern and that this pattern
would be different from control children or other isolated developmental disorders
(reading disorders, math disorders or ASD).
Table 6.1. Characteristics of the samples.Samples sizes, number of boys and age.
Sample 1: WISC-RNL Male Age
N N (%) Mean (SD) Isolated Epilepsy 39 22 (56.4) 12.3 (1.9) Isolated Reading Disorder 29 19 (65.5) 12.6 (0.8) Isolated math Disorder 27 18 (66.7) 12.9 (1.2) Isolated ASD 24 23 (95.8) 12.2 (1.3)
Sample 2: WISC-IIINL Male Age N N (%) Mean (SD)
Isolated Epilepsy 100 48 (48.0) 10.0 (2.6) Epilepsy + Reading Disorder 31 20 (64.5) 9.6 (2.7) Epilepsy + Math Disorder 17 5 (29.4) 9.2 (2.0) Epilepsy + ASD 21 18 (85.7) 9.4 (3.2) Control 81 40 (49.4) 9.4 (1.7)
Note. ASD = autism spectrum disorders. Sample 1 consists of children with epilepsy, reading, math or autism spectrum disorders “in isolation”. Sample 2 consists of non-referred control children, children with epilepsy in isolation, and children with reading, math or autism spectrum disorders comorbid with epilepsy.
The second research question addressed VIQ – PIQ discrepancies for children with
isolated epilepsy versus epilepsy with comorbid disorders. The aimed at studying (a) to
what extent isolated epilepsy and epilepsy with comorbid conditions differ in VIQ – PIQ,
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
88
and (b) whether VIQ – PIQ patterns in epilepsy depend on the type of comorbid disorder.
Do children with epilepsy show a different cognitive pattern in the light of comorbidities
like reading disorders, math disorders or autism spectrum disorders? Do developmental
disorders present with different patterns when accompanied by epilepsy? The second
hypothesis was that in the light of comorbidities, cognitive patterns will appear altered. If
this is the case, it will provide better understanding of the inconsistent results reported on
the literature. That is, if cognitive patterns in isolated epilepsy are different from patterns
seen in epilepsy with comorbidities, the variation in findings on VIQ – PIQ patterns could
be due to variation across samples reported in the literature in the type and proportion of
comorbid disorders. If patterns in comorbidities appear altered, the finding will also have
implications for the clinical diagnosis of the comorbidity and for its remediation. The
present study was based on two samples, one with isolated conditions and one with
comorbid conditions.
Methods
Participants
Except for the control children, all participating children had been referred for
special services including psychological assessment because of developmental concerns.
The children with epilepsy came from a tertiary centre for epilepsy and from a school
which provided special services to children with epilepsy associated to the centre and had
heterogeneous epileptic conditions. The children with specific learning disorders and
ASD came from schools providing special educational services for learning disorders and
for children with psychiatric and behavioural disorders, respectively. The Wechsler IQ
data for the current study were gathered from the files of the schools and the epilepsy
centre; over time, two different test versions of the Wechsler were used. For each version,
there were not sufficient numbers of children to fill each disorder (reading, math, ASD)
by comorbidity (with or without comorbid epilepsy) condition. Therefore, for this study
two separate samples were used. Sample 1 was tested with the Dutch version of the WISC-
R (to be called WISC-RNL), and consisted of four groups of children matched for age: 39
with isolated epilepsy, 29 with a reading disorder, 25 with a math disorder, and 24 with
an autism spectrum disorder. A portion of the first sample has been described earlier (van
Iterson & Kaufman, 2009).
PATTERNS IN COMORBIDITIES
89
Sample 2, tested with the Dutch WISC-III (WISC-IIINL), included 171 children with
epilepsy and 81 non-referred control children. The control children came from regular
schools and were included only if no disabilities were suspected by parents or teachers
and if their FS-IQ was between 76 and 130. The sample of 171 children with epilepsy
consisted of four groups of children: 100 with epilepsy without a comorbid disorder, 31
with a comorbid reading disorder, 19 with a comorbid math disorder, and 21 with
comorbid ASD. The majority of children were not included in samples reported upon in
earlier publications. However, in order to maintain adequate sample sizes of the children
with comorbidities, 9 children (5.3% of the present sample) were included which
overlapped with an earlier study (van Iterson et al., 2014).
Children were included only if they had taken the complete Wechsler Intelligence
Scales for Children and had a FS-IQ above 75. It should be noted that while FS-IQ was a
criterion for eligibility for special services for children with specific learning disorders
and for children with ASD, patterns displayed by the scales (e.g. VIQ – PIQ discrepancy)
was not.
Children with epilepsy had a confirmed diagnosis of epilepsy by a neurologist or
child epileptologist. Information on epilepsy was obtained from medical reports and
related to seizure type (focal or generalized seizures), side of seizure onset, localisation,
presence of abnormalities on neuroimaging (MRI+), epilepsy syndrome, age at onset of
epilepsy (AOE) and number of AEDs used. Epilepsy syndrome severity was rated on a
10-point scale (Dunn, Buelow, Austin, Shinnar, & Perkins, 2004), where 10 was the most
severe. Duration of epilepsy was calculated as the difference between AOE and age at
testing.
For inclusion in a sample with learning disorders, an expert in special education
verified the presence of significant and persistent achievement problems on the domains
of reading/spelling or math; or a qualified psychologist provided a diagnosis of specific
reading or math disorder. Children with both reading and math disorders were excluded.
C
OG
NIT
IVE
PATT
ERN
S IN
PA
EDIA
TRIC
EPI
LEPS
Y
Tabl
e 6.
2. S
eizu
re c
hara
cter
istic
s of t
he sa
mpl
es w
ith e
pile
psy.
Sa
mpl
e 1
Sa
mpl
e 2
Epile
psy
isol
ated
Ep
ileps
y is
olat
ed
Epile
psy
and
Rea
ding
Dis
. Ep
ileps
y an
d M
ath
diso
rder
s Ep
ileps
y an
d A
SD
M
ean
SD
Mea
n SD
M
ean
SD
Mea
n SD
M
ean
SD
N
39
100
31
19
21
Test
ver
siso
n W
ISC
-RN
L W
ISC
-IIIN
L W
ISC
-IIIN
L W
ISC
-IIIN
L W
ISC
-IIIN
L A
ge a
t ons
et o
f epi
leps
y 6.
7 4.
6 6.
0 3.
0 6.
2 3.
2 5.
3 2.
4 5.
7 3.
7 D
urat
ion
epile
psy
to te
st
5.7
3.7
4.1
3.2
3.4
2.4
3.9
3.0
3.6
2.1
AED
s trie
d 2.
4 1.
5 2.
2 1.
3 2.
0 1.
8 1.
9 1.
3 2.
3 1.
9 Ep
ileps
y sy
ndro
me
seve
rity
5.1
1.4
5.4
1.7
5.2
1.7
5.6
1.7
5.1
0.8
n
%
n %
n
%
n %
n
%
Seiz
ure
type
Gen
eral
ized
seiz
ures
6
15.4
24
24
.0
10
32.3
6
31.6
4
19.0
A
bsen
ces /
Aty
pica
l abs
ence
s 2
/ 1
5.1
/ 2.6
11
/ 3
11.0
/ 3.
0 3
/ 0
9.7
/ 0.0
3
/ 0
15.8
/ 0.
0 0
/ 0
0.0
/ 0.0
To
nic
clon
ic se
izur
es /
Myo
clon
ic
seiz
ures
0
/ 0
0.0
/ 0.0
2
/ 1
2.0
/ 1.0
1
/ 2
3.2
/ 6.5
1
/ 0
5.3
/ 0.0
2
/ 0
9.5
/ 0.0
Se
vera
l gen
eral
ized
seiz
ure
type
s 1
2.6
5 5.
0 3
9.7
1 5.
3 2
9.5
Gen
eral
ized
not
spec
ified
2
5.1
2 2.
0 1
3.2
1 5.
3 0
0.0
Fo
cal (
all f
ocal
) 25
64
.1
56
56.0
15
48
.4
11
57.9
15
71
.4
(Als
o) te
mpo
ral /
(als
o) fr
onta
l 6
/ 11
15.4
/ 28
.2
22 /
13
22.0
/ 13
.0
5 / 1
1 16
.1 /
35.5
1
/ 8
5.3
/ 42.
1 3
/ 5
14.3
/ 23
.8
(Als
o) p
arie
tal /
(als
o) c
entra
l 2
/ 5
5.1
/ 12.
8 10
/ 16
10
.0 /
16.0
1
/ 4
3.2
/ 12.
9 0
/ 1
0.0
/ 5.3
1
/ 1
4.8
/ 4.8
(A
lso)
occ
ipita
l 7
17.9
9
9.0
4 12
.9
1 5.
3 1
4.8
Foca
l: LH
/ R
H
7 / 5
17
.9 /
12.8
22
/ 13
22
.0 /
13.0
4
/ 7
12.9
/ 22
.6
4 / 1
21
.1 /
5.3
6 / 2
28
.6 /
9.5
Bila
tera
l of m
utifo
cal
13
33.3
21
21
.0
4 12
.9
6 31
.6
7 33
.3
U
ncer
tain
/ un
know
n 3
/ 5
7.7
/ 12.
8 19
/ 1
19.1
/ 1.
0 5
/ 1
16.1
/ 3.
2 1
/ 1
5.3
/ 5.3
2
/ 0
9.5
/ 0
MR
I - /
MR
I +
32 /
7 82
.1 /
17.9
83/1
7 83
.0 /
17.0
27/ 4
87
.1 /
12.9
16/ 3
84
.2 /
15.8
18/ 3
85
.7 /
14.3
90
PATT
ERN
S IN
CO
MO
RB
IDIT
IES
Ta
ble
6.2.
Sei
zure
cha
ract
eris
tics o
f the
sam
ples
with
epi
leps
y (c
ontin
ued)
.
Sa
mpl
e 1
Sa
mpl
e 2
Epile
psy
isol
ated
Ep
ileps
y is
olat
ed
Epile
psy
and
Rea
ding
Dis
. Ep
ileps
y an
d M
ath
diso
rder
s Ep
ileps
y an
d A
SD
Epile
psy
synd
rom
es
n %
n
%
n %
n
%
n %
Foca
l idi
opat
hic
BEC
TS /
Rol
andi
c ep
ileps
y (2
)a 3
7.7
5 5.
0 2
6.5
BEO
P / P
anay
otop
olou
s (3)
1
2.6
1 1.
0 1
3.2
1 4.
8
Foca
l sym
ptom
atic
B
y vi
rtue
of: e
tiolo
gy (5
) / lo
caliz
atio
n (7
) 5
/ 13
12.8
/ 33
.3
11 /
36
11.0
/ 26
.0
3 / 1
0 9.
7 / 3
2.3
1 / 5
5.
3 / 2
6.3
2 / 8
9.
5 /3
8.1
Cry
ptog
enic
loca
lisat
ion
rela
ted
(6.5
) 6
15.4
5
5.0
4 21
.1
1 4.
8
Gen
eral
ized
epi
leps
y
Gen
eral
ized
idio
path
ic
CA
E (3
) / JM
E (5
) 1
/ 0
2.6
/ 0.0
12
/ 2
12.0
/ 2.
0 5
/ 1
16.1
/ 3.
2 3
/ 0
15.8
/ 0.
0 0
/ 1
0.0
/ 4.8
O
ther
gen
er id
iopa
thic
epi
not
def
ined
abo
ve (5
) 3
7.7
11
11.0
3
9.7
4 19
.5
C
rypt
ogen
ic a
nd/o
r sym
ptom
atic
W
est s
yndr
. (9.
5) /
nons
peci
fic o
ther
aet
iolo
gy (
8)
1 / 0
1.
0 / 0
.0
0 / 1
0.
0 / 3
.2
1 / 2
5.
3 / 1
0.3
Ep
ileps
y sy
ndro
mes
und
eter
min
ed (f
ocal
/gen
eral
ized
)
Ep
ileps
y w
ith C
SWS
(8)
8 8.
0 2
6.5
LKS
, aty
pic
Rol
andi
c, p
seud
o Le
nnox
(8)
7 7.
0 1
3.2
1 5.
3
Oth
er (
1 - 9
) 3
7.7
3 3.
0
Unk
now
n 4
10.3
8
8.0
2 6.
5 2
10.3
4
19.0
91
C
OG
NIT
IVE
PATT
ERN
S IN
PA
EDIA
TRIC
EPI
LEPS
Y
Not
e. a =
epi
leps
y sy
ndro
me
seve
rity
scor
e in
bra
cket
s acc
ordi
ng to
Dun
n et
al.,
200
4.
Epile
psy
isol
ated
= e
pile
psy
and
cogn
itive
con
cern
s bu
t no
com
orbi
d di
agno
sis
of r
eadi
ng, m
ath,
ASD
. Epi
leps
y +
read
ing
= ep
ileps
y an
d
com
orbi
d re
adin
g di
sord
ers,
epile
psy
+ m
ath
= ep
ileps
y an
d co
mor
bid
mat
h di
sord
ers,
epile
psy
+ A
SD =
epi
leps
y an
d co
mor
bid
ASD
, AED
s
tried
= n
umbe
r of
ant
i-epi
lept
ic d
rugs
trie
d; S
ever
al g
ener
aliz
ed s
eizu
re t
ypes
= e
.g.,
myo
clon
ic s
eizu
res
and
abse
nces
, (A
lso)
tem
pora
l =
tem
pora
l sei
zure
s re
porte
d, p
ossi
bly
in a
dditi
on to
sei
zure
s fr
om a
noth
er lo
calis
atio
n, e
.g.,
tem
pora
l and
occ
ipita
l; L
H /
RH
= le
ft he
mis
pher
e
or ri
ght h
emis
pher
e se
izur
e on
set,
MR
I-: n
o ab
norm
aliti
es o
n ne
uroi
mag
ing
or n
o ne
uroi
mag
ing
avai
labl
e, M
RI+
: abn
orm
aliti
es re
porte
d on
neur
oim
agin
g. B
ECTS
: Ben
ign
Epile
psy
with
Cen
tro-T
empo
ral S
pike
s, B
EOP
= B
enig
n Ep
ileps
y w
ith O
ccip
ital P
arox
ysm
, TC
= to
nic
clon
ic
seiz
ures
, CA
E =
Chi
ldho
od A
bsen
ce E
pile
psy;
JM
E =
Juve
nile
Myo
clon
ic E
pile
psy;
CSW
S =
cont
inuo
us s
pike
and
wav
es d
urin
g sl
ow s
leep
,
LKS
= La
ndau
-Kle
ffne
r syn
drom
e. N
ote
that
rate
s pre
sent
ed u
nder
the
head
ing
of se
izur
e ty
pes a
nd u
nder
epi
leps
y sy
ndro
mes
may
som
etim
es
appe
ar in
cong
ruen
t (e.
g., a
typi
cal a
bsen
ce se
izur
es m
ay b
e cl
assif
ied
as b
elon
ging
to a
foca
l syn
drom
e, fo
cal s
eizu
res m
ay b
e se
en in
CSW
S).
92
PATTERNS IN COMORBIDITIES
93
Inclusion in the sample with specific learning disorders was based on three criteria, for
reading and math alike: (a) severity, defined as achievement scores below the 7th
percentile on reading, spelling or both reading and spelling for a specific reading
disorder, and on mathematics for a specific math disorder; (b) insufficient response to
intervention, i.e., persistence over time in spite of special remediation measures; and (c)
achievement not explained by a low IQ, for which FS-IQ > 75 was required. These
criteria have been maintained over time in The Netherlands in order to qualify for
diagnoses of specific learning disabilities (Pijl & Pijl, 1998; Resing et al., 2002; van Luit,
Bloemert, Ganzinga, & Mönch, 2012).
Children were included in the sample of children with ASD if they had a diagnosis
by a psychiatrist or by a qualified mental health psychologist according to DSM-IV
criteria. Diagnoses on autism, pervasive developmental disorders (PDD-NOS) or
Asperger syndrome as well as broad diagnoses of ASD were pooled into the diagnosis of
ASD. Three (14.3%) children in Sample 2 had been diagnosed with Asperger syndrome,
all others with ASD or PDD-NOS. Children with ASD and another behavioural
comorbidity (e.g., ADHD) were excluded. Table 6.1 shows data on age and sex of the two
samples. Table 6.2 displays data on the epilepsy characteristics for the groups with
epilepsy.
Wechsler Test Versions
Sample 1 was tested with the Dutch adaptation of the WISC-R (to be called WISC-RNL, van
Haasen et al., 1986), in use up to 2005. Sample 2 and the sample of control children, were
tested with the most recent WISC version in the Netherlands, the Dutch WISC-III (WISC-
IIINL, Wechsler, 2005). The test versions share the same two-scale structure, the verbal and
performances scales, and are composed of 5 verbal and 5 performance core subtests with
the same names. Both test versions also share two factor indexes verbal comprehension
index (VCI) and perceptual organization index (POI), which consist of the same subtests
(de Bruyn, Vandersteene, & van Haasen, 1986; Sattler, 1990, 2001; Wechsler, 2005). The
third factor, however, differs between the two samples: freedom from distractibility (FD)
is included in the WISC-RNL, and processing speed (PSI) in the WISC-IIINL. While the focus
in the present paper was on VIQ and PIQ, and VCI and POI will be reported as well, the
role of PSI on the pattern will be considered only briefly. The subtest substitution was
converted to a deviation quotient in order to provide an indication of speed for all
children (Table 6.3).
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
94
Analyses
The analyses for the two samples were run in parallel. For both samples,
ANOVAs with repeated measures were conducted with IQ scale (VIQ or PIQ, and VCI or
POI, respectively) as within-subject variable and type of disorder (epilepsy, reading
disorder, math disorder and ASD) as between-subject variable. Simple contrasts followed
to compare epilepsy with the other disorders. Thereafter, using ANOVA with planned
contrasts, post hoc analyses were conducted directly on the discrepancy scores (VIQ –
PIQ, VCI – POI) to contrast isolated epilepsy to each of the other disorders. The analyses
were repeated with age and sex as covariates. Similarly, an ANOVA with repeated
measures was done on the factor triad VCI – POI – PSI, to study the effect of processing
speed on isolated epilepsy in the second sample only. In addition, in the second sample
verbal and nonverbal abilities of isolated epilepsy were contrasted with those of the non-
referred control sample.
Results
This section starts with preliminary comparisons on age, sex, epilepsy variables and IQ of
the various groups. Then, results of repeated measures ANOVA are presented in which
VIQ – PIQ and VCI – POI patterns across groups were examined. The means and
standard deviation on the various IQ scales for the various disorders and for the control
groups are presented in Table 6.3. The results of the statistical analyses are reported in
Table 6.4.
The results on the cross-group differences in the VIQ – PIQ difference will be
presented in four sections. (1) First, differences in VIQ – PIQ pattern between the non-
referred control group and isolated epilepsy (Sample 2); (2) differences in VIQ – PIQ
pattern in isolated epilepsy versus isolated reading disorders, isolated math disorders and
isolated ASD (Sample 1); (3) differences in VIQ – PIQ pattern for isolated epilepsy
versus epilepsy with comorbid reading disorders, comorbid math disorders and comorbid
ASD (Sample 2); (4) differences in VCI – POI – PSI pattern in isolated epilepsy (Sample
2) to determine the role of processing speed in epilepsy; and (5) an exploratory overall
analysis of both samples that examines the independent contributions of type of disorder
(reading, math or ASD) and comorbid epilepsy status (absent or present) on the difference
between VIQ and PIQ. Results on VCI and POI are reported in Table 6.4 but will only be
described if the results differ from those for VIQ – PIQ.
PATTERNS IN COMORBIDITIES
95
Table 6.3. Means and SDs on the WISC-RNL and the WISC-IIINL.
Sample 1 Sample 2 "Isolated disorder" Disorder comborbid
with epilepsy Epilepsy Reading Math ASD Epilepsy Reading Math ASD Control
FS-IQ Mean 93.0 93.1 93.5 92.5 90.5 94.0 85.9 94.3 103.0 SD 11.0 11.5 9.1 10.0 11.4 10.1 9.6 13.6 10.7
VIQ Mean 96.4 90.5 93.9 91.6 93.8 94.6 91.6 96.1 102.5 SD 10.9 11.9 8.6 10.1 12.1 10.3 8.5 13.8 11.0
PIQ Mean 90.8 97.8 95.6 94.4 88.4 95.0 82.9 93.6 103.1 SD 13.5 13.1 11.1 14.6 12.4 10.8 11.9 12.9 12.4
VIQ–PIQ Mean 5.6 -7.3 -1.6 -2.8 5.3 -0.5 8.7 2.5 -0.6 SD 13.9 13.8 11.5 16.6 13.5 11.1 11.4 11.7 13.7
VCI Mean 98.0 89.7 92.8 92.5 94.8 95.3 95.6 96.8 102.7 SD 12.0 11.9 10.0 10.3 11.5 11.0 9.7 13.0 11.7
POI Mean 92.9 96.1 92.4 95.6 90.0 95.9 83.1 96.1 103.3 SD 13.3 12.4 12.3 17.1 12.4 10.4 12.4 12.8 12.5
VCI–POI Mean 5.1 -6.4 0.3 -3.0 4.8 -0.6 12.6 0.7 -0.6 SD 15.5 12.7 15.3 19.4 13.6 12.6 11.4 12.1 13.4
SU Mean 89.0 97.1 94.0 90.0 90.4 96.1 91.1 89.0 103.0 SD 11.4 15.8 12.4 12.9 14.5 13.2 10.9 13.3 15.3
PSI Mean 91.1 96.8 89.0 87.9 104.1 SD 14.6 13.2 12.8 13.8 14.8
VCI–PSI Mean 3.7 -1.6 6.6 8.8 -1.4 SD 17.1 14.3 16.5 13.2 18.2
POI–PSI Mean -1.1 -0.9 -5.9 8.1 -0.86 SD 15.7 15.2 16.1 15.6 18.3
Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, PSI = Processing Speed Index (WISC-IIINL only). SU = subtest substitution converted into a deviation quotient.
Preliminary analyses
Comparison of isolated epilepsy between Sample 1 and 2 with t or chi-square
tests showed that the first sample was older at the age of testing (t = 5.64, p < .001, d =
1.16) and the duration of epilepsy was also longer (t = 2.45, p = .015, d = .43). Otherwise,
statistically significant differences were not seen for sex, any epilepsy variable or any of
the Wechsler scales or factor indexes. MANCOVA, adjusting for age and duration of
epilepsy, revealed that the two samples with isolated epilepsy showed highly similar
patterns of verbal and performance abilities (for VIQ – PIQ, F(1, 132) = 0.035, p = .852
and for VCI – POI, F(1,132) = 0.003, p = .958). Similar results were found when the
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
96
square root of the duration of epilepsy (van Iterson et al., 2014) was entered in the
MANCOVA instead of duration of epilepsy.
Within each sample there were no age differences across disorder groups. For
Sample 2, ANOVA and chi-square tests did not reveal statistically significant differences
across the four epilepsy groups for any of the epilepsy variables. Chi-square tests showed
that boys were overrepresented in ASD in Sample 1 (χ2 (3) = 11.2, p = .011) and Sample
2 (χ2 (3) = 17.1, p = .001). Comparison for each disorder between Sample 1 and 2 showed
similar sex ratios for reading disorders and ASD. However, girls were overrepresented in
comorbid math (Sample 2) relative to isolated math disorders (Sample 1; χ2 (1) = 7.50, p
= .006). ANOVA and ANCOVA revealed that there were no differences in FS-IQ
between Samples 1 and 2 before or after adjusting for differences in age and sex.
Epilepsy in isolation versus non-referred controls
Repeated measures ANOVA (Table 6.4.3. showed a significant main effect for
group (F(1,179) = 62.27, p < .001, η2p= 0.26). The control children outperformed the
children with isolated epilepsy. A main effect was seen for the IQ scales, indicating that
VIQ was higher than PIQ (F(1,179) = 5.38, p = .022, η2p= 0.03). In addition, the
interaction of VIQ and PIQ by group was significant (F(1,179) = 8.64, p = .004, η2p=
0.05; for VCI and POI F(1,179) = 7.03, p = .009, η2p= 0.04). The epilepsy group had
higher verbal than performance abilities while the control sample had a flat pattern of
verbal and performance abilities. Thus, the results indicated that the control children had
higher overall scores on the Wechsler test, and that the children with epilepsy had a VIQ
> PIQ pattern not seen in the controls. There were no age and sex differences between
samples.
PATT
ERN
S IN
CO
MO
RB
IDIT
IES
Tabl
e 6.
4.1.
Res
ults
of re
peat
ed m
easu
res A
NO
VA
. Sam
ple
1.
Re
peat
ed m
easu
res A
NO
VA
Post
hoc
anal
yses
95
% C
I 95
% C
I 95
% C
I
df1
df2
F p
η p2
Con-
trast
SE
p U
L LL
Co
n-tra
st SE
p
UL
LL
Con-
trast
SE
p U
L LL
Sam
ple
1: Is
olat
ed D
isord
ers
Epile
psy
vs R
eadi
ng
Ep
ileps
y vs
Mat
h
Epile
psy
vs A
SD
VIQ
PIQ
W
ithin
subj
ects
VIQ
vs P
IQ
1 11
6 1.
35 .
247
0.01
Be
twee
n su
bjec
ts D
isord
er
3 11
3 0.
15 .
930
0.00
0
.53
2.49
.8
33
-4.4
5.
5 1.
11 2
.47
.654
-3
.8
6.0
-0
.63
2.63
.81
2 -5
.8
4.6
Inte
ract
ion
VIQ
vs P
IQ *
diso
rder
3
113
4.99
.00
3 0.
12
-12.
89 3
.43
<.00
1 -1
9.7
-6.1
-7
.26
3.59
.04
6 -1
4.4
-0.1
-8
.45
3.63
.02
2 -1
5.6
-1.3
V
CI P
OI
With
in su
bjec
ts V
CI v
s PO
I 1
116
0.48
.49
1 0.
00
Betw
een
subj
ects
Diso
rder
3
113
0.58
.63
2 0.
02
-2.5
6 2.
50
.310
-7
.5
2.4
-2.8
4 2.
51
.260
-7
.8
2.1
-1.3
7 2.
65
.605
-6
.6
3.9
Inte
ract
ion
VCI
vs P
OI *
diso
rder
3
113
3.25
.02
5 0.
08
-11.
53 3
.84
.003
-1
9.1
-3.9
-4
.76
4.03
.2
40
-12.
7 3.
2 -8
.12
4.07
.0
48
-16.
2 -0
.1
97
C
OG
NIT
IVE
PATT
ERN
S IN
PA
EDIA
TRIC
EPI
LEPS
Y
Tabl
e 6.
4.2.
Res
ults
of r
epea
ted
mea
sure
s AN
OV
A. S
ampl
e 2.
R
epea
ted
mea
sure
s AN
OV
A
Po
st h
oc a
naly
ses
95%
CI
95%
CI
95%
CI
df1
df2
F p
η p2
Con
-tra
st
SE
p U
L LL
C
on-
trast
SE
p
UL
LL
Con
-tra
st
SE
p U
L LL
Sa
mpl
e 2:
Com
orbi
ditie
s
Is
olat
ed E
pile
psy
vs E
pile
psy
and
Rea
ding
Dis
orde
rs
Is
olat
ed E
pile
psy
vs
Epile
psy
and
Mat
h D
isor
ders
Is
olat
ed E
pile
psy
vs
Epile
psy
and
ASD
V
IQ P
IQ
With
in su
bjec
ts
VIQ
vs P
IQ
1 17
0 11
.32
.001
0.0
6 B
etw
een
subj
ects
D
isor
der
3 16
8 3.
00 .
032
0.05
3
.70
2.08
.0
78
-0.4
7.
8 -3
.82
2.52
.13
2 -8
.8
1.2
3.7
7 2.
43
.123
-1
.0 8
.6
Inte
ract
ion
VIQ
vs P
IQ *
dis
orde
r 3
168
2.57
.05
6 0.
04
-5.7
9 2.
61
.028
-1
1.0
-0.6
3
.34
3.17
.29
3 -2
.9
9.6
-2.8
2 3.
05
.358
-8
.8 3
.2
VC
I PO
I W
ithin
subj
ects
V
CI v
s PO
I 1
170
12.5
6 .0
01 0
.07
Bet
wee
n su
bjec
ts
Dis
orde
r 3
168
2.56
.05
7 0.
04
3.19
2.0
3 .1
18
-0.8
7.
2 -3
.05
2.45
.21
5 -7
.9
1.8
4.0
1 2.
37
.092
-0
.7 8
.7
Inte
ract
ion
VC
I vs P
OI *
dis
orde
r 3
168
4.61
.00
4 0.
08
-5
.46
2.69
.0
44
-10.
8 -0
.1
7
.77
3.27
.01
8 1
.3
14.2
-4.1
0 3.
14
.194
-1
0.3
2.1
98
PATT
ERN
S IN
CO
MO
RB
IDIT
IES
Ta
ble
6.4.
3. R
esul
ts o
f rep
eate
d m
easu
res A
NO
VA
. Sam
ple
2.
R
epea
ted
mea
sure
s AN
OV
A
Po
st h
oc a
naly
ses
95
% C
I
df1
df2
F p
η p2
Con
-tra
st
SE
p U
L LL
Sa
mpl
e 2:
Iso
late
d Ep
ileps
y vs
Con
trols
Isol
ated
Epi
leps
y vs
Con
trols
V
IQ P
IQ
With
in su
bjec
ts
VIQ
vs P
IQ
1 17
9 5.
38
.022
0.0
3 B
etw
een
subj
ects
G
roup
1
179
62.2
7 <.
001
0.26
11
.71
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99
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
100
Verbal and performance abilities in isolated conditions. Epilepsy, Reading
Disorders, Math Disorders and ASD
Repeated measures ANOVA showed that the main effect of disorder group was
not significant (Sample 1, see Table 6.4.1), indicating that overall IQ scores were
highly similar across groups. Also, the overall difference between VIQ and PIQ was
not significant. But, as expected, there was a significant disorder by IQ-scale (VIQ –
PIQ) interaction, indicating that the discrepancy between VIQ and PIQ differed across
disorder groups. Planned contrasts revealed a significant difference in VIQ – PIQ
pattern between epilepsy and each of the disorders (reading disorder, math disorder
and ASD). In epilepsy, VIQ was higher than PIQ, whereas in the other disorders the
VIQ – PIQ pattern was opposite or flat. These results did not change when the
analyses were redone with age and sex as covariates.
Verbal and performance abilities in epilepsy. Isolated epilepsy and epilepsy
comorbid with Reading Disorders, Math Disorders and ASD
Repeated measures ANOVA revealed a significant main effect of disorder
group suggesting differences in overall IQ across groups (Sample 2, see Table 6.4.2).
Post hoc tests revealed no IQ differences when the comorbid conditions (epilepsy
with reading disorder, math disorder or ASD) were compared to isolated epilepsy.
Children with a comorbid reading disorder, however, outperformed children with a
comorbid math disorder (FS-IQ of 94.0 versus 85.9).
The interaction effect of disorder by IQ scale (VIQ – PIQ) fell short of
statistical significance (p = .056). However, the interaction of disorder by factor index
(VCI – POI) was significant indicating that the VCI – POI difference varied across
groups. Using planned contrasts, isolated epilepsy was compared to each comorbid
condition. A significant difference in VCI – POI pattern was found between the group
with isolated epilepsy and the group with epilepsy with comorbid reading disorders
(similar to the results for VIQ – PIQ). The higher VCI than POI pattern found in
isolated epilepsy was not seen in the group with an additional reading disorder.
Comparison of isolated epilepsy and epilepsy with a comorbid math disorder showed
that the VCI – POI difference was significantly higher in the group with a comorbid
math disorder (but not for the VIQ – PIQ discrepancy). No differences were found in
VCI – POI pattern between isolated epilepsy and epilepsy with comorbid ASD.
Overall, the results of these analyses suggest that in the sample with epilepsy, verbal
PATTERNS IN COMORBIDITIES
101
abilities were higher than performance abilities. However, this pattern is qualified by
a comorbid reading or math disorder. In comorbid reading disorders, the VIQ > PIQ
pattern is not found, while in math disorders, the VIQ > PIQ pattern seems
exacerbated. Comorbid ASD did not affect the pattern of verbal and performance
abilities. The results did not change when the analyses were redone with age and sex
as covariates.
The role of processing speed
Repeated-measures ANOVA with polynomial contrasts was conducted on the
factor triad VCI – POI – PSI in isolated epilepsy. It was hypothesized that, if lowered
performance abilities would be mainly due to lowered speed of processing, the factor
PSI, with its high reliance on speed, should be the lowest in the pattern and a linear
downward pattern should emerge. The analysis revealed a pattern best described as
quadratic (F (1,99) = 6.11, p = .015, ηp2 = 0.58); a linear pattern was also supported (F
(1,99) = 4.74, p = .032, ηp2 = 0.46). That is, the VCI was highest, and POI as well as
PSI were lowered, but PSI was relatively less lowered. Pairwise contrasts indicated
that VCI was higher than both POI (t = 3.53, p = .001, d = 0.43) and PSI (t = 2.18, p =
.032 , d = 0.28). No significant difference was seen for POI – PSI (t = -0.69, p = .492).
The impact of epilepsy and of diagnostic condition on verbal - performance
patterns
Results from Sample 1 and Sample 2 indicate that in isolated epilepsy PIQ is
lower than VIQ. In the other disorders (Sample 1), such pattern is not seen. In reading
disorders, VIQ is relatively lower than PIQ; and in math disorders and ASD, VIQ is
approximately equal to PIQ. In comorbid disorders (Sample 2), however, the VIQ –
PIQ patterns are different from the VIQ > PIQ pattern that characterizes isolated
epilepsy. In comorbid reading disorders, the pattern is relatively flat, and in comorbid
math disorders, an even greater VIQ > PIQ discrepancy emerges. In ASD, no major
changes are seen.
In a final exploratory analysis, we examined whether these VIQ – PIQ
differences across disorders (reading disorders, math disorders and ASD) were
similarly affected by status of epilepsy (presence or absence). For this analysis, the
disorder groups of the two samples were taken together. The control children (Sample
2) and the children with isolated epilepsy (Samples 1 and 2) were excluded. Sample 1
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
102
and Sample 2 differed in the type of Wechsler test that was administered. In taking
together groups from both samples, it is assumed that different types of Wechsler tests
do not affect the VIQ – PIQ discrepancies, although they may have an impact on mean
IQ differences. This assumption appears warranted, given that VIQ – PIQ
discrepancies were highly similar across Samples 1 and 2. The assumption will be
further considered in the Discussion. A 2 * 3* 2 factorial analysis was done, which
included two IQ scales (VIQ and PIQ) by three disorders (reading disorder, math
disorder, ASD) by two values of status of epilepsy (present or absent). Thereafter, the
results were redone including age and sex as covariates and no major changes were
seen on the main effects.
There was no significant difference in overall IQ between the isolated and
comorbid disorder (F(1,143) = 1.05, p = .308), which indicated there was no overall
effect of epilepsy status on IQ. There was also no significant difference in overall IQ
level across disorders (F(2,143) = 1.76, p = .175). However, the disorder by epilepsy
interaction was significant (F(2,143) = 3.11, p = .047, η2p= 0.04), due to a lower
overall IQ for math disorder comorbid with epilepsy.
Overall, the difference between VIQ and PIQ was not significant (F(1,143) =
.024, p = .877). More interestingly, significant interactions were found with epilepsy
and with type of disorder. The interaction of VIQ – PIQ with epilepsy status (F(1,143)
= 12.35, p = .001, η2p= 0.08) was due to a VIQ < PIQ pattern in the isolated disorders
and a VIQ > PIQ pattern in the comorbid disorders. The interaction between VIQ –
PIQ and disorder (F(2,143) = 4.19, p = .017, η2p= 0.06) indicated differences in VIQ –
PIQ patterns across disorders, irrespective of epilepsy status. Most importantly, the IQ
scales by disorder by epilepsy interaction was not significant (F(2,143) = 0.44, p =
.647), indicating that the VIQ – PIQ discrepancies across the disorders were similar
for the isolated and comorbid disorders, given the VIQ > PIQ pattern seen in epilepsy.
Put differently, the VIQ – PIQ discrepancy in each of the disorders is affected in a
similar way by the comorbid presence of epilepsy. This can also be seen in Figure 6.1,
where the lines are largely parallel, suggesting a systematic shift in the difference
between VIQ and PIQ due to comorbid epilepsy. A similar shift is visible in Figure
6.1 between the control group and the group with isolated epilepsy.
PATTERNS IN COMORBIDITIES
103
Figure 6.1.
Means after adjusting for age and sex with MANCOVA for VIQ – PIQ discrepancies (left) and VCI – POI (right) for the data of the two studies. No (add) diag = no (additional) diagnoses, denotes “isolated” epilepsy and controls. Open squares: children with isolated epilepsy (= no additional diagnosis), reading disorders (= Reading), math disorders (= Math) and ASD. Filled triangles = non-referred control sample (WISC-IIINL). Filled diamonds: children with epilepsy: isolated epilepsy, or epilepsy comorbid with reading disorders, math disorders and ASD. Note that positive values – values in the upper part of the figure – denote lowered performance/perceptual abilities and negative values – in the lower part of the figure – denote lowered verbal abilities, while overall FS-IQs do not differ.
Discussion
The present study further supported the finding that the cognitive pattern of referred
children with isolated epilepsy – that is, epilepsy without an additional diagnosis – is
characterized by relatively spared verbal abilities and relatively depressed
performance abilities (VIQ > PIQ). This pattern is not seen in children with isolated
neurodevelopmental disorders – reading disorders, math disorders, ASD – of similar
overall IQ. In children who had two conditions diagnosed jointly – epilepsy and either
reading disorders, math disorders or autism spectrum disorders – patterns are different.
control control
-10
-5
0
5
10
15
No (add)diag reading math ASD
No (add)diag reading math ASD
VIQ - PIQ VCI - POIΔ
in IQ
poi
nts
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
104
The VIQ > PIQ pattern appears mitigated in reading disorders and exacerbated in
math disorders.
Alternatively, from the perspective of the learning or behavioural condition,
the results suggest that in the presence of epilepsy, the cognitive patterns of
neurodevelopmental disorders are altered. This change is the direction of relatively
lowered performance abilities or relatively spared verbal abilities. A strength in the
performance abilities seen in isolated reading disorders appears levelled off; a flat
pattern seen in isolated math disorders is changed into a VIQ > PIQ pattern in
comorbid math disorders. Supplementary exploratory analyses further suggested that
the impact of epilepsy on VIQ – PIQ discrepancies is similar across the various
disorders. These results might suggest a common mechanism from the seizure
condition impinging on the comorbid neurodevelopmental disorder.
The study shed new light on the previous equivocal findings on the VIQ – PIQ
pattern in children with epilepsy, suggesting that patterns vary according to the
presence of children with comorbidities. By carefully checking for comorbid
disorders, the present study found higher verbal than performance/perceptual abilities
in isolated epilepsy. However, VIQ > PIQ patterns in isolated epilepsy were altered
when epilepsy appeared with a comorbidity. These results illustrate that VIQ – PIQ
differences across samples of children with epilepsy can differ if comorbidity with
other disorders is not taken into account.
The VIQ > PIQ pattern in isolated epilepsy was different from the pattern
observed in other isolated disorders. Depressed VIQ was seen in reading disorders,
and relatively flat VIQ – PIQ patterns were seen in math disorders and ASD, overall
in accordance with previous studies (de Bruin et al., 2006; Desoete, 2008; Pelletier et
al., 2001; Wechsler, 2005). Earlier studies comparing children with epilepsy with
other referred children have reported similar differences in cognitive patterns between
the referred children with and without epilepsy (Nicolai et al., 2012; van Iterson &
Kaufman, 2009; Vermeulen & Aldenkamp, 1995).
A VIQ > PIQ pattern was also found in an earlier study on paediatric epilepsy
(van Iterson et al., 2014), particularly in early onset epilepsy and in the early years of
the epilepsy, and fading away over time. The lowered scores on PIQ subtests might be
caused by impairments in visual perceptual abilities, perceptual reasoning, or
constructional and motor abilities, which are all involved in the subtests constituting
PIQ (and POI). Also, several of these subtests are timed, which means that quicker
PATTERNS IN COMORBIDITIES
105
speed leads to better scores. Earlier studies have, indeed, pointed toward lowered speed
and executive abilities in children with epilepsy (Gottlieb, Zelko, Kim, & Nordli, 2012),
impaired visual perceptual reasoning, visual attention, sustained attention, motor
abilities and motor speed (Bhise, Burack, & Mandelbaum, 2010; Braakman et al.,
2012). Lowered scores on speed, executive and visual tasks have also been reported in
new-onset epilepsy before the start of medication (Hermann et al., 2006; Oostrom, van
Teeseling, Smeets-Schouten, Peters, & Jennekens-Schinkel, 2005; Rathouz et al., 2014)
and persisting over time (Rathouz et al., 2014). Lowered scores on processing speed
were also seen in the present work. The most depressed scores were seen, however, on
the performance/ perceptual abilities, suggesting that the more complex constructional
abilities tapped by the scales may be most vulnerable to the epileptic condition, where
speed may be one of the constituents leading to low scores.
Compromised performance abilities have also been seen in children born
prematurely (Lee, Yeatman, Luna, & Feldman, 2011), children with traumatic brain
injury (Babikian & Asarnow, 2009), and children with lateralized perinatal brain
damage, regardless of the side of the lesion (Ballantyne, Spilkin, Hesselink, & Trauner,
2008). These results suggest that the performance scale appears particularly vulnerable
to neurological risks, including epilepsy.
An interesting finding of the present study is that comorbid epilepsy in various
disorders appeared associated with a similar “systematic” shift in the difference
between VIQ and PIQ compared to these disorders in isolated form. Though potentially
important, this finding should be considered with some caution. First, it should be
acknowledged that the sample sizes were probably too small to detect small interaction
effects. Second, the finding is based on combination of samples tested with two
different versions of the Wechsler scales and with an age difference of 3.1 years. In
merging the data into a final analysis, it was assumed that differences in test version and
age would not influence the results. Changes in test version can potentially be
associated with changes in verbal – performance patterns, given that Flynn effects may
affect the subtests differentially (Kaufman, 2010). In a previous study on epilepsy,
however, which explicitly modelled for effects of test version (Dutch WPPSI-R, WISC-R
and WISC-III) on VIQ – PIQ patterns, no effect of test version was seen (van Iterson et
al., 2014).
With regard to age differences, earlier studies on children with isolated reading
disorders, math disorders and autistic spectrum disorders with younger children or with
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
106
wider age ranges were congruent with the present results (de Bruin et al., 2006;
Desoete, 2008; Pelletier et al., 2001; Wechsler, 2005). Also, studies on serial testing
of referred children reported negligible mean differences (Canivez & Watkins, 1998)
in VIQ – PIQ discrepancy over time. A follow-up study on non-referred children
reported moderate to high correlations for the VIQ – PIQ discrepancy across ages, and
a higher stability over time for VIQ < PIQ discrepancies than for VIQ > PIQ
discrepancies (Moffitt & Silva, 1987). Therefore, although results should be treated
with caution, there are also arguments suggesting that differences in age and test
version did not unduly affect the results. Notably, the samples with isolated epilepsy –
regardless of age and test version – showed conspicuous similarities in cognitive
patterns.
In line with current knowledge, both epilepsy as well as the comorbidities are
understood as complex, multidimensional conditions in terms of aetiology and
presentation (Berg et al., 2010; Pennington, 2006; Walsh, Elsabbagh, Bolton, &
Singh, 2011). The epilepsy and the comorbidity may be independent conditions, or
they may be related conditions partly sharing underlying risk factors (Brooks-Kayal et
al., 2013). Several models on the causes of comorbidities have been proposed (Pal,
2011) which may all be valid in particular cases. According to one model, the seizure
condition could be understood as the cause of the comorbid disorder. The epileptic
networks could be interfering with cognitive networks involved in reading (for
example), causing a reading disorder (Pal, 2011). In this model, the VIQ – PIQ pattern
in a particular isolated disorder would be similar to the pattern of this disorder
comorbid with epilepsy – a finding not supported by the current study. A second
model suggests that there may be one or more causes leading to the epilepsy as well as
the comorbidity, which may present alone or in combination. According to the third
model (Pal, 2011), epilepsy and the comorbidity may or may not share a common
cause, but epilepsy might impact on the comorbidity, for example by aggravating it.
The current study was not designed to test these models, and does not permit
conclusions about their validity. However, the present study may contribute to the
understanding of comorbidities in epilepsy, suggesting that in familiarly unrelated
cases, isolated neurodevelopmental disorders show different cognitive patterns from
patterns in isolated epilepsy, and when neurodevelopmental disorders present together
with epilepsy, an altered cognitive pattern is seen relative to the isolated condition.
Cognitive patterns seen in isolated disorders appear systematically shifted towards
PATTERNS IN COMORBIDITIES
107
relatively lowered performance abilities (or relatively spared verbal abilities) when
they co-occur with epilepsy.
A limitation of the study is the inclusion of two samples. Although efforts were
done to select the children with developmental problems according to objective
criteria, new insights and new assessment tools develop over time and sharpen
diagnostic criteria for classification (Pijl & Pijl, 1998; Resing et al., 2002), leading to
differences. Some criteria remained stable over time, as the 7th percentile criterion to
determine a true weakness in learning disorders, even if subtyping of disorders has
progressed. In the diagnosis of ASD, the earlier reliance on subtypes as PDD-NOS
and Asperger subtype is leading to a broad categorization of “ASD”, possibly with the
advent of DSM-V. Despite these unavoidable differences, cognitive patterns found for
the isolated disorders resembled those described in the literature.
Overall, the present study suggests that in isolated epilepsy, the cognitive
pattern is characterized by VIQ > PIQ. In other developmental disorders, such a
pattern was not seen. When these disorders appear as comorbidities in epilepsy, the
patterns are altered, partly resembling the isolated condition, and partly differing from
the isolated condition. In clinical evaluations of children with epilepsy, and
independent of epilepsy syndrome, the possibility of comorbidities should be
considered. The most relevant clinical implication of the present study is that the
cognitive pattern seen in the disorder comorbid with epilepsy is likely to differ from
the pattern seen in the isolated condition. One possibility is that the difficulties
encountered by the child with epilepsy may be associated with specific “subtypes”, of
the disorder. It may be speculated that children with epilepsy and reading disorders,
problems with rapid naming may be more prominent than phonological disorders.
Regardless of whether the starting point is epilepsy or another developmental
disorder, if the disorder is accompanied by epilepsy, the clinician should take into
consideration that the cognitive pattern may be unlike the pattern seen in the isolated
condition. Remediation measures should therefore be tailored to fit the individual
profile of the child with epilepsy and a comorbid diagnosis.
Discussion
DISCUSSION
111
Discussion
The overarching topic of the present work was the pattern of verbal and nonverbal
development found in referred children with epilepsy. It dealt with intra-individual
differences – that is, high levels of functioning on one cognitive domain or one measure
and low levels on the other – between IQ scales, between subtests and between serial
measurements. While the main sample of interest was the sample with epilepsy, for some
studies, clinical comparison children with other developmental disorders or typically
developing control children were included as well. The research questions were all
addressed with Dutch versions of the same diagnostic instrument: the Wechsler
Intelligence Scales for Children.
Overview of Major Findings
This section will start with an overview of the findings of the diverse chapters and some
words on consistenncies and inconsistencies between the results. Thereafter, in the next
section, the patterns of cognitive development and cognitive change that emerge from
these findings will be detailed.
Subtest scatter.
The Introduction gave a brief outline on epilepsy in children in relation to
cognition and listed the research questions. Thereafter, in Chapters 2 and 3, intra-
individual subtest variability or “subtest scatter” was discussed. Subtest scatter was
compared across samples with neurodevelopmental disabilities on the verbal,
performance and full scales of the Wechsler Intelligence test for Children to study
whether elevated scatter is a sign of pathology, and to study whether it is elevated in
epilepsy. The results indicated that while intra-individual subtest variability was elevated
in clinical samples, elevated scatter was not an overall sign of pathology. Rather, large
intra-individual subtest variability was seen in some developmental disorders, but not in
others. It was seen mainly in children with psychiatric disorders on performance IQ and
full-scale IQ. Within psychiatric disorders, large intra-individual subtest variability was
seen particularly in children with autism spectrum disorders, where it was seen on all
scales. Children with learning disabilities were less likely to show increased variability.
Children with epilepsy as a whole did not show increased subtest scatter. Subsets of
children with epilepsy, however, specifically children with left hemisphere seizures, were
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
112
prone to show increased scatter on the verbal scale. This was especially so when
abnormalities were present on neuroimaging. Children with right hemisphere onset
seizures and MRI abnormalities, on the other hand, showed decreased variability on the
verbal and full scale.
Reliable Cognitive Change.
Chapter 4 aimed at establishing whether rates of individuals showing reliable
cognitive change in IQ scores over time were elevated in epilepsy. Children with epilepsy
were tested two times with the same test version with a mean interval of 2.3 years.
Cognitive change was tested against predetermined 90% cut-off scores (5% of scores
beyond this interval reflecting reliable cognitive gain, 5% reflecting cognitive loss) based
on data coming from referred children with neurodevelopmental disorders, but without
epilepsy. It was found that children with epilepsy were likely to show reliable cognitive
change in higher rates than expected for referred children. This change was seen mainly
as decline; the rates of cognitive gains did not differ from those expected. Decline was
seen on the verbal scale in 26% of the children and on the full scale in 16.4 %. This is a
fivefold rate of cognitive loss on the verbal scale, and a threefold rate of cognitive loss on
the full scale for children with epilepsy relative to referred children without epilepsy. On
the performance scale, rates were not found to be elevated (5.5%).
Relation of pattern of cognitive change to epilepsy variables.
In Chapter 5, a longitudinal study was conducted to describe the pattern of
cognitive change over time as a function of duration of the seizure condition and other
epilepsy variables associated with severity of the condition. A differential impact on the
verbal compared to the performance scale was found. The performance scale showed
already relatively lowered scores at initial testing; thereafter a further decline was seen.
The verbal scale was initially “spared” and showed a steep decline followed by more
gradual decline that continued for a prolonged period of time. On both scales, the largest
decline is seen in the first 40 to 50 months after the onset of epilepsy; thereafter the
decline was less pronounced. Early age at onset of epilepsy and longer duration were
associated with more decline. Other epilepsy-related variables associated with severity of
epilepsy, however, failed to show a relation with cognitive decline over time. Inclusion in
special education was associated with lower IQ scores, but not with different patterns of
decline. Likewise, higher parental education was associated with higher IQ scores, but
was not “protective” against decline. While the overall pattern was characteristic for
cognitive decline over time at group level, large variability was seen among children.
DISCUSSION
113
Comorbidities and the VIQ – PIQ discrepancy
In Chapter 6, the verbal and performance scales and the VIQ – PIQ discrepancy
were addressed once again to study the impact of comorbidities in epilepsy on cognitive
patterns. Besides children with isolated epilepsy, the study also included typically
developing control children, children with other developmental disorders and children
with comorbidities in epilepsy. Patterns in isolated epilepsy were studied in relation to
patterns seen in isolated learning disorders and ASD, and in relation to epilepsy comorbid
with learning disorders or ASD. “Isolated” disorders were those which were not
accompanied with a second diagnosis; the child may have had other cognitive or
behavioural problems but these did not qualify for a diagnosis. Children with epilepsy in
isolation showed a pattern which differed from the pattern seen in non-referred control
children, and in children with isolated reading disorders, math disorders and autism
spectrum disorders. A VIQ > PIQ pattern (with a mean discrepancy of 5 to 6 IQ points)
was seen in epilepsy but not in children with other disorders – and not in control children.
In typically developing children, the pattern between verbal and performance abilities was
flat, and in other developmental disorders a tendency for a VIQ < PIQ pattern was seen
(most clearly in reading disorders in which VIQ was 7.3 points lower than PIQ).
However, the pattern was different when epilepsy was accompanied by a comorbidity. In
epilepsy and comorbid math disorders, the advantage of the verbal scale seen in epilepsy
became more conspicuous; while in epilepsy and reading disorders (and epilepsy with
ASD), the advantage for the verbal scale was no longer seen. When the comorbidities
were taken as a starting point in the interpretation, it could be seen that the impact of the
epilepsy on the comorbidity was always in the direction of relatively “less lowered”
verbal abilities or relatively “less spared” performance abilities. The impact of epilepsy
on comorbidities seemed similar across the various diagnoses, suggesting a shared impact
of epilepsy on all conditions.
Consistencies and conflicting findings across studies.
Two conflicting findings could be seen across studies. First, while an effect of
seizure lateralization and of aetiology was found to be associated with intra-individual
subtest variability as scatter (Chapters 2 and 3), no association was found between these
epilepsy variables and verbal – performance discrepancies. Why would epilepsy variables
affect scatter but not verbal – performance discrepancies? Is the finding that seizure
lateralization or brain lesions influenced scatter a true finding? One approach to
understanding these findings would assume that there is no true difference and that
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
114
methodological differences across studies led to different results. Chapters 2 and 3 were
written according to a frequently applied methodology: various groups (here: left versus
right hemisphere onset seizure, presence or absence of lesions) were compared on a single
dimension (scatter) after ensuring that no major differences existed between possibly
confounding variables (like age at onset, age at testing). While valid and often applied,
such an approach may fail to account for the smaller cumulative effects of these variables,
and therefore lead to results which differ from studies which account for these variables
including them in the model, like regression-based analyses as applied in Chapter 5. This
means that more research is needed to replicate the results of Chapter 3, studying the
effects of test version, as well as of duration of epilepsy. The results on Chapter 3 provide
some indications that these variables may also be of influence on scatter. The other
approach, however, would assume that there is a true difference and that scatter is indeed
influences by seizure lateralization. In the next section it will be discussed that the
reorganization that takes place in the epileptic brain leads to sparing of verbal abilities
and possibly to more scattered patterns of verbal abilities – literally reflected in elevated
scatter in lesional left hemisphere seizures.
The second seemingly incongruent finding is that no elevated rates of children
showing reliable cognitive change on the performance scale was seen in Chapter 4, while
Chapter 5 reports a protracted decline on the performance scale over time. This
incongruence is easily solved. The cut-off score needed to qualify for reliable cognitive
change on the performance scale is 18 points, larger than the 14 needed on the verbal or
full scales, while performance IQ is already lowered at the time of first measurement.
More importantly, the children in Chapter 4 were followed for a period of time as short as
2.3 years, while the children in Chapter 5, who were slightly younger at first
measurement, were followed for a period of 2.8 years and some of them for as long as 5.1
years. Indeed, when a longer period is taken between test and retest, and cut-off scores are
adjusted for changes in test version, losses on the performance scale appear as well: 19%
of a sample of 26 children showed reliable loss on the performance scale (van Iterson &
Augustijn, 2013).
Otherwise, the results of the various studies appear remarkably consistent. Most
important, the inclusion of new subjects in Chapter 6, not reported upon in the earlier
chapters, yielded highly similar results (verbal > performance patterns) as the data in
earlier chapters (Chapters 2 and 5).
DISCUSSION
115
A Pattern of Cognitive Function and Cognitive Change in Children with Epilepsy
Taken together, a pattern of cognitive function and cognitive change over time in
children with epilepsy emerges – not described earlier. Children with epilepsy referred to
a tertiary centre due to concerns about their development are likely to show overall
lowered intellectual abilities compared to children without epilepsy and without
developmental concerns. These lowered abilities have been reported in children assessed
within the first months after the onset of epilepsy (B. Hermann et al., 2006), and could be
seen at first testing (Chapters 4, 5 and 6). IQs may often be unexpectedly low, and may be
discrepant with the school type the child was initially enrolled in (van Iterson, 2010),
suggesting that early in the course of epilepsy significant changes occur. These changes
have sometimes already been detected prior to the onset of the epilepsy in terms of school
failure (Hermann, Jones, Jackson, & Seidenberg, 2012; Schouten, Oostrom,
Jennekens-Schinkel, & Peters, 2001). At first testing, the full-scale IQ appears somewhat
lowered, but the two subscales are differentially affected. Soon after the epilepsy
surfaces, the performance IQ can be seen to be already depressed, while verbal IQ is
relatively “spared” (Chapter 5 and 6). It is suggested that the verbal scale at first testing
may be reflecting IQ scores that are closer in magnitude to the original, premorbid
cognitive potential of the child. The performance scale may be a better indicator of the
vulnerable reaction of the brain to the emerging seizure condition.
The sparing of verbal abilities.
Language abilities are known to be processed in their majority by the left
hemisphere. Particularly in young children, language abilities have shown great resilience
to brain disruptions. The most noticeable example is the fact that language functions can
be taken over by other brain areas in cases of severe disruption or brain development. A
change in lateralization of language functions is seen in children with early, e.g.,
prenatally, acquired left hemisphere brain lesions, when the non-affected right
hemisphere participates in language even to the extent of taking over most of the
language functions (Lidzba, Staudt, Wilke, & Krageloh-Mann, 2006; Loring et al., 1999;
Staudt et al., 2002). It signals the plasticity of the brain when it comes to retain and
“spare” the verbal abilities in the human being. Children with epilepsy may have
identifiable brain lesions, but most children with epilepsy do not; further, most children
with epilepsy, even in surgical series, will have language functions preserved in the left
hemisphere (Blackburn et al., 2007). There is an increasing body of evidence, however,
that children with epilepsy, including children with epilepsy syndromes of low severity,
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are likely to show abnormal patterns of brain development (Braakman et al., 2013;
Hutchinson et al., 2010; Overvliet et al., 2013). Early sparing of verbal functions as
manifested by relatively high verbal IQ may point to a similar mechanism of
“prioritizing” the preservation of verbal abilities, not only in children with prenatal brain
lesions, but also in children with epilepsy. Although it remains speculative, this plasticity
in favour of the linguistic domain may partly explain why verbal abilities are relatively
resilient to epilepsy, particularly in children with early onset of epilepsy.
Of interest, the relative sparing of verbal abilities in epilepsy has also been found
in verbal memory tasks. Children with epilepsy have been found to outperform typically
developing children in verbal list learning in several studies, even if they show lowered
scores on virtually all other neuropsychological tasks, including visual memory
(Braakman et al., 2012; Høie, Mykletun, Waaler, Skeidsvoll, & Sommerfelt, 2006).
The vulnerability of the performance abilities.
The present work leads us to suggest that lowered performance abilities could
possibly be seen as an early cognitive marker of the vulnerability of the brain to the
underlying seizure condition. It is still incompletely understood why the performance
scale is most vulnerable to the seizure condition in its earliest stages and it is unknown
whether performance abilities may already have been lowered before the emergence of
the seizures. It has been suggested that latent changes occur in the brain before the onset
of epilepsy (Hermann et al., 2010). When the epilepsy surfaces, non-specific cognitive
problems become evident, which affect attention, executive functions, constructional
abilities, and visual-motor speed (Bhise, Burack, & Mandelbaum, 2010; Fastenau et al.,
2009; Hermann, Jones, Jackson, & Seidenberg, 2012; Hermann et al., 2006), even before
medical treatment is started (Bhise, Burack, & Mandelbaum, 2010). These abilities
underlie the performance scales, and may give rise to the lowered scores particularly on
performance abilities, and lead to the VIQ > PIQ pattern. The performance scale may also
be relying more on the ability of the child to deal with problem solving in novel
situations, and as such the epilepsy may be interfering with the ability to deal with
novelty in an adequate and speedy manner; it is possible that an intact functioning brain is
needed for performance on these tasks. VIQ > PIQ patterns have also been described in
other neurological samples, like children with prenatal acquired unilateral brain lesion,
regardless of side of lesion (Ballantyne, Spilkin, Hesselink, & Trauner, 2008; Lidzba et
al., 2006), and in children born preterm (Lind et al., 2011), confirming the vulnerability of
the performance scale in children with neurological conditions.
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The VIQ > PIQ pattern may be considered at least to some extent as specific for
epilepsy and other children with neurological disorders. The pattern is different from non-
referred controls and from children with other developmental disorders. Typically
developing children show a flat pattern of cognitive abilities, while children with reading
disorders, math disorders and autism spectrum disorders tend to show “spared”
performance abilities, at least to some degree (Chapter 6). Epilepsy may co-exist with
another developmental disorder, like reading, math and ASD (Russ, Larson, & Halfon,
2012), and in these cases, the performance abilities also seem to be less spared.
Patterns at subtest level – the issue of scatter.
While at the level of the scales a difference between verbal and performance
abilities is observed in epilepsy, at the subtest level (Chapters 2 and 3), for the group with
epilepsy as a whole, elevated intra-individual variability (subtest scatter) is not seen.
Rather, subtest scatter was dependent on epilepsy variables like lateralization and brain
lesion and could be either increased (in lesional LH seizures) or decreased (in lesional RH
seizures), levelling off the scores of the whole group.
Increased subtest variability had been found in adults with brain abnormalities and
normal IQ (Ryan, Tree, Morris, & Gontkovsky, 2006) and in mentally deteriorating adults
in an early stage of the disorder (Reckess, Varvaris, Gordon, & Schretlen, 2014). These
findings suggest that, at least in adults, the combination of a neurological condition and
relatively spared cognitive abilities may lead to increased intra-individual subtest
variability. Children with brain lesions are likely to show a trajectory of brain
reorganization. In LH lesions, such reorganization may include changes in language
lateralization in some children (Lidzba et al., 2006) and, it may tentatively be said, that
these changes are associated with less consistent or more variable responses on the
different tasks sampled in the verbal scale, and therefore, more scatter.
From a psychometric point of view, a similar rationale could be given for verbal –
performance discrepancies and elevated scatter. In the standardization group of typically
developing children, the verbal and performance scale correlate substantially with each
other, the scales show high internal consistency, and the subtests within the scale show
variable – but overall high – correlations with the scale. The seizure condition and the
concomitant reorganizations of the brain may be reflecting themselves on the intelligence
scales as a “loosening” of the internal consistency within and between the subscales,
leading to overall lower correlations and larger differences among the various subtests. In
left hemisphere seizures, sparing of verbal abilities can be seen, but at the expense of a
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118
scattered profile of verbal abilities; in right hemisphere seizures, a VIQ > PIQ pattern can
also be seen, without elevated scatter. This remains, however, a speculative explanation.
The children included in Chapter 3 showed great heterogeneity in brain lesions and the
observation that verbal scatter is increased or decreased in epilepsy depending on side of
brain lesion remains insufficiently understood and awaits replication.
Changes in patterns over time.
Over time, as the child with epilepsy grows older and the duration of epilepsy
increases, the pattern shown by the verbal and performance scale changes (Chapter 5).
The performance IQ, which was already low at the beginning, declines at a slow pace.
The verbal IQ, on the other hand, declines as well. This decline is faster in the earlier
years and goes on at a progressively slower pace over time – but continues over a
prolonged period of time. The detrimental impact of duration of epilepsy, particularly on
verbal abilities, has been seen in other studies as well (Caplan et al., 2008; Lopes et al.,
2013). Some authors have suggested that children with epilepsy may not keep up with the
increasing demands on integration of linguistic abilities and complex thinking as they
grow older, therefore showing lowered verbal scores (Addis, Lin, Pal, Hermann, &
Caplan, 2013). Particularly in older children developing epilepsy, previously acquired and
consolidated knowledge may generally remain preserved, but the epileptic condition may
be interfering with the acquisition of newer and more complex information. Given that
the intelligence test requires higher levels of knowledge and proficiency as the child
grows older, the failure to develop at the same pace as other children is reflected as a
lowered verbal IQ.
The lowered IQ over time seen in children with epilepsy may be interpreted as
having various gradations. First, most children with seizures retain most of the
consolidated knowledge and continue to develop, but at a slower pace than earlier. IQ
appears lowered over time (Chapter 5). As found in Chapter 4, while elevated rates of
children were seen showing cognitive loss, it can also be stated that even in a sample with
relatively complicated epilepsy, most children maintained an IQ within the boundaries
required to speak of “no reliable change in cognitive function”. Second, a significant
subset of children will show a significant deceleration of cognitive development and
learning, which will be detected by the intelligence scales as reliable cognitive loss.
Third, some children may lose abilities acquired earlier and, therefore will also exhibit
cognitive loss on the scales. The last two groups, those with a strong deceleration of
development and those with true loss of cognitive function, show reliable cognitive
DISCUSSION
119
change at retesting. Finally, some children may show a temporary loss followed by
recovery, and repeated testing over time may sample moments within this process of
losing, maintaining or recovering abilities.
With increased duration of epilepsy, the VIQ > PIQ pattern becomes less
conspicuous and tends to disappear. Decreased VIQ – PIQ gaps in children as a function
of duration of epilepsy, has also been observed elsewhere (Blackburn et al., 2007). When
the seizure condition remits, cognitive changes have occurred and are likely to continue –
at least in a portion of the children. Over time, the cognitive abilities seen in the child will
differ substantially from the premorbid abilities, both in terms of cognitive level and in
terms of cognitive pattern. This changing pattern is likely to have major impact on the
school career of the affected individual. Level of intelligence, and particularly verbal IQ,
is known to be strongly associated with school achievement (Glutting, Watkins, Konold,
& McDermott, 2006; van Haasen et al., 1986; Watkins & Glutting, 2000). Indeed, in a
study on secondary school children, failure to progress in school was found to be
associated with significant lowering of IQ relative to estimated premorbid IQ (van
Iterson, 2010). This failure to progress at school was seen in repetition of grades, being
set back to a lower type of school, or both. Lower VIQ and FS-IQ were also associated
with participation in special education (Chapter 5).
Some authors have described epilepsy as a life-long condition, with consequences
surpassing those of the seizures themselves and affecting long-term social outcome (C. S.
Camfield & Camfield, 2007). The changing level and the changing pattern imply that not
only the child, but also parents, schools and teachers have to adapt to the new level of
functioning as well as to the new pattern of strengths and weaknesses.
The Role of Epilepsy Variables.
Earlier studies on heterogeneous samples of epilepsy that used comprehensive
models failed to identify the impact of specific epilepsy variables on cognitive outcome
(Braakman et al., 2012; Oostrom, van Teeseling, Smeets-Schouten, Peters, & Jennekens-
Schinkel, 2005; Reijs et al., 2007). Overall, the present studies were in line with earlier
studies. For most epilepsy variables no clear impact on cognitive level or cognitive
change over time was found. Epilepsy variables influencing the verbal – performance
discrepancy are not easily identified. Most epilepsy variables did not show a relation with
pattern of IQ. For example, when topographical localization of seizure onset (frontal
versus temporal) was entered as a variable in the analysis reported in Chapter 5, no
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120
differential impact was seen on cognitive level or cognitive course over time as a function
of seizure localization. This is congruent with studies reporting that dysfunctions
generally known as “frontal dysfunctions” may also be seen in children with temporal
lobe seizures, like attention and executive functions (Campiglia et al., 2014; Riccio,
Pliego, Cohen, & Park, 2014; Rzezak et al., 2007).
The present work failed to find a significant effect of syndrome severity. This lack
of association may be, at least partly, due to “restriction of the range” of syndrome
severity. On one end of the continuum, in the present work, more children with moderate
and severe epilepsies were included and relatively fewer children with epilepsies of low
severity. On the other side of the continuum of severity, only children who were able to
participate in (repeated) testing were included. Omitting children who were unable to take
the test may have excluded children with most severe syndromes. It is known that
children with epilepsy may show unstable courses of remission and relapse (C. Camfield,
Camfield, Gordon, Smith, & Dooley, 1993), and the present sample likely also included
such children, obscuring the possible effects of seizure freedom on cognitive
development.
Also, no significant role of medication on cognitive pattern and cognitive changes
was found when the number of antiepileptic drugs (AEDs) tried was included in the
analyses (Chapter 5). The role of number of AEDs used at the time of neuropsychological
testing was analyzed on children tested on the factor scores of the WISC-IIINL (van Iterson
& Augustijn, 2014). That study showed that a verbal > perceptual pattern could be seen
for the children with epilepsy, regardless of whether they were on or off medication. An
effect of medication appeared only in children using two or more antiepileptic drugs,
suggesting lowered processing speed. The results were in line with studies showing that
the impact of the seizure condition on cognitive function is larger than the added impact
of the AED. Only after the 3rd drug, the impact of AED on cognitive functions, like
executive functions and speed of processing, becomes evident (Witt & Helmstaedter,
2013).
The present studies also revealed some contribution of distinct epilepsy variables.
These concerned mainly time-related variables.
Age at onset of epilepsy: Age at onset shows an overlap with seizure syndrome
and epilepsy syndrome severity given that many epileptic syndromes have a time window
wherein they appear: most epileptic encephalopathies, considered the worst epilepsies,
surface in the first years of life (Covanis, 2012). In a study that examined age at onset of
DISCUSSION
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epilepsy in association with response to medication, (Berg, Zelko, Levy, & Testa,
2012), early age of onset was associated with poor outcome only in children whose
seizures did not remit with medication. In the present study, with children with more
complicated epilepsies, age at onset was a fair estimator of cognitive trajectory over time
and, therefore, a fair marker of severity.
Duration: Duration of epilepsy was a significant predictor of cognitive level and
of cognitive decline over time. The best measure of duration was not linear, but
logarithmic. This means that soon after the onset of epilepsy, decline is most pronounced.
Thereafter, it continues in an increasingly slower pace – decline levels off. These results
imply that information about the duration of the seizure condition should be added to all
studies on epilepsy in children. In terms of epilepsy, the passing of time is characterized
by seizure remission in some children, a pattern of remissions followed by relapses in
other children, and no seizure control in still others (Geerts et al., 2010). The current
study presented a pattern of decline where no significant contribution of active versus
inactive seizures was found. Interpretation of these results is aided by the studies that
indicate lasting changes in brain development and brain organization, including
connectivity and neuronal density in children with various epileptic conditions, like
centro- temporal spikes or frontal lobe seizures (Braakman et al., 2013; Kanemura, Sano,
Tando, Sugita, & Aihara, 2012; Overvliet et al., 2013). Based on cases with duration of
epilepsy up to almost three years, Kanemura (2012) highlighted that seizures from frontal
lobe origin are associated with disturbance of prefrontal brain growth over time. Already
with this relatively short duration of seizures, a relationship between duration of seizures
and level of disturbed brain growth could be seen. Also, this disturbed brain growth lasted
beyond seizure remission. Thus, long-term changes in cognitive functions seen in the
present work may be the cognitive counterpart of the long-term changes in brain
development.
Overall, recent studies, including the present work, converge towards showing that, at
least to some extent, epilepsies of various degrees of severity, share neuropsychological
outcome patterns. There is an increasing number of publications reporting abnormalities
in brain structure and decreased or abnormal connectivity between brain areas in children
with epilepsy, expanding well beyond the areas involved in seizure generation and lasting
beyond seizure remission (Braakman et al., 2013; B. P. Hermann et al., 2010; Hutchinson
et al., 2010; Kanemura et al., 2012; Overvliet et al., 2013). The findings that brain
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122
abnormalities occur at areas distant to the primary epileptogenic area may explain why
epilepsy variables like side and topographical site of epilepsy fail to show relationships
with cognitive level, cognitive pattern and cognitive course over time, and why long-
lasting cognitive changes can be seen in children with epilepsy. In addition, from the
literature it becomes increasingly clear that various (genetic) causes may clinically
converge to a single epileptic syndrome, as in juvenile myoclonic epilepsy (Koepp,
Thomas, Wandschneider, Berkovic, & Schmidt, 2014), and also that epileptic syndromes,
even those with a shared cause, can manifest as a spectrum, with various degrees of
severity (Rudolf, Valenti, Hirsch, & Szepetowski, 2009). These findings are in line with
the present results showing that cognitive decline may be associated will all kinds of
epilepsy.
The Role of Comorbidities.
In addition to time-related epilepsy variables, comorbidities also were found to
impact on the pattern of abilities. Without making reference to possible causative factors,
the results presented in Chapter 6 may be discussed within the framework of models of
comorbidities described in the literature (Pal, 2011). One model of Pal assumed that
comorbidities are caused by the epilepsy. Epileptic networks could be interfering with
cognitive networks related to a particular ability (e.g., reading or math), leading to a
reading or math problem. Some cases with comorbidities, like subsets of children with
math disorders or subsets of children with ASD, which may be characterized by higher
verbal than performance abilities, would fit this explanatory model. Overall, however, the
present data are not in line with this model and suggest that the cognitive networks
vulnerable to seizures are more likely the networks involved in the performance abilities,
rather than those involved in the verbal abilities, at least during the early stages of the
seizure condition.
According to a second model (Pal, 2011) the epilepsy and the behavioural disorder
may share risk factors, which may be present at any level of (biological) development
(Brooks-Kayal et al., 2013). The disorders may appear either in combination or alone, as
can be seen in families with members having either an isolated seizure disorder, an
isolated (language) disorder, or both (Clarke et al., 2007). Chapter 6, which included
familiarly non-related cases, suggests that the seizure condition and the behavioural
disorder show specific cognitive patterns. When seizure condition and comorbidity co-
occur in an individual, cognitive outcome patterns tend to have characteristics of both
DISCUSSION
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disorders and, up to some extent, they seem to be the additive result of combining the
patterns of both isolated disorders.
The third model (Pal, 2011) is similar to the second, adding that when a
developmental disorder and epilepsy co-occur, they impact on each other, for example by
worsening the cognitive outcome. Worse outcomes in comorbidities have indeed been
reported (Hermann, 2008). It could have been expected that, for example in reading
abilities, a lowered verbal IQ characteristic of the reading ability, together with a lowered
performance IQ, characteristic epilepsy, would lead to an overall depressed IQ. Full-scale
IQ did not appear particularly lowered in comorbidities. Focus on the epileptic condition
may lead to under-referral for comorbidities (Helmstaedter et al., 2014). It is conceivable,
that in children with a relatively spared IQ lack of progress in a specific school area may
be reason for referral for diagnosis of specific learning problems; in contrast, in children
with epilepsy and a low IQ, lack of progress is more likely to be ascribed to the seizure
condition. In the present work, the lowered IQ was seen in comorbid math only (due to
lowered PIQ). The present data, however, indicate that epilepsy and comorbidity impact
on the cognitive pattern, showing characteristics of both disorders.
Limitations and Assets of the Studies
The data in the present studies were collected in clinical settings on an individual
basis from clinically referred children over a protracted period of time. This type of data
collection is associated with limitations and methodological challenges to ensure
generalizability of results.
Clinical data collection. Data on clinical groups are observational data, collected
in the clinical situation. If afterwards information is missing, for example because new
insights have demonstrated the relevance of specific variables, the missing information
can hardly be collected in retrospect. For example, data on parental education were not
available for the children tested earlier, given that data gathering on social economic
status was considered “not done” in the first stages of data collection. This means a
limitation in the presentation and interpretation of the data. An early decision was made
to collect data on the Wechsler Intelligence Scales from the children evaluated
systematically throughout a prolonged period of time. Doing so generated the research
hypotheses which were tested thereafter. Overall, however, similar results were found in
the present studies regardless of whether the newer or the older version of the WISC had
been used.
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Single centre study. Participants with epilepsy were all collected within the
context of the tertiary epilepsy centre SEIN and its associated epilepsy school De
Waterlelie with its national school service centre, LWOE. This raises the question
whether the findings can be generalized to the rest of the country. The answer to this
question can confidently be affirmative: the two tertiary epilepsy centres which operate
nationwide can be considered largely equivalent; the two schools work together in the
services they provide. In addition, data coming from elsewhere, for example data on first
testing on children retested at the centre, were also included. Similarly, as detailed in the
next paragraph, the results should generalize to nations other than The Netherlands,
provided that the topic of interest is mixed samples of children with epilepsy referred to
tertiary centres due to developmental concerns.
Clinically referred children. In the present series, the children with epilepsy
included were all clinically referred children, for whom concerns about cognitive
development had risen and a risk for cognitive problems was seen. While the results of
the present series may not be valid for samples with uncomplicated epilepsies without
cognitive problems (B. Hermann et al., 2008; Jones, Siddarth, Gurbani, Shields, &
Caplan, 2010), the data may well generalize to the large number of children whose
epilepsy is not uncomplicated. Surveys conducted on large samples of children with
seizures show that the co-occurrence of epilepsy and school-related difficulties, far from
being exceptional, is very common. For example, Russ et al. (2012) presented data
showing that up to almost 75% of children with epilepsy acceded special (education)
services – which implies that large percentages had cognitive, learning or behavioural
problems, or combinations of these problems. The samples in the present work included a
clinically relevant broad spectrum of children, likely to represent these 75% which
needed assessment and specialized educational services.
Test versions. The present work refers mainly to two different test versions of the
WISC: the WISC-RNL and the WISC-IIINL. The WISC-IIINL is the most recent WISC available in
The Netherlands. Other countries however, like the US and the UK, have changed to the
WISC-IV (Wechsler, 2004), and the norm data collection for the standardization of the
WISC-V started in the US a few years ago (Alan S. Kaufman, 28.7.2013, San Diego,
personal communication), and the test was published in 2014. In this light, some WISC
data may be considered “historical”. After more than 60 years, the structure of the new
Wechsler test has undergone major changes. The WISC-IV no longer has the option for
calculating verbal and Performance IQ; instead the factor index scores verbal
DISCUSSION
125
comprehension and perceptual reasoning are given. In addition, the WISC-IV has a
Processing Speed factor and a Working Memory factor. The perceptual reasoning factor
of the WISC-IV has a lower reliance on motor dexterity and speed than either the
performance scale or the perceptual organzation factor of the WISC-III. The WISC-V took
this development towards more “pure” neuropsychological measures a step further, by
splitting the perceptual reasoning index into two factors – Visual Spatial and Fluid
Reasoning. The newest test versions will allow more fine-grained analyses of the
strengths and weaknesses of children with developmental disorders.
In spite of these changes, the verbal and performance-perceptual abilities have
continued to be the two core domains in cognitive measurement, also in the newest test
versions (Flanagan & Kaufman, 2009; Wechsler, 2004; Weiss, Saklofske, Schwartz,
Prifitera, & Courville, 2006). It remains open to what extent the results presented in this
work would have been different, had newer test versions been used. Interestingly, a recent
study on children with frontal and temporal seizures also presented data suggesting a
verbal > perceptual pattern of cognitive abilities (Campiglia et al., 2014).
The implications of these changes in test versions for the study of epilepsy are not
yet clear. For example, WISC-III studies have shown that the Working Memory index
and the Processing Speed index are affected in epilepsy (Berg et al., 2008b). While
of interest, such a pattern is unlikely to be specific for epilepsy, as there is evidence that
they can also be found in well-delineated samples with ADHD, as well as in mixed
samples with developmental disabilities (Devena & Watkins, 2012).
There are considerations in favour of the inclusion of traditional test versions.
Newer test versions are being developed at a quicker pace in the US and UK than in other
countries. Earlier WISC versions, like the WISC-III, are still being used internationally, and
if several test versions yield similar results (as seen for the WISC-R and WISC-III in terms of
cognitive patterns), generalizability of the results to other test versions or languages is
increased.
Second, it should be considered that the “broader” measures as included in the
traditional tests may have a higher ecological validity. For example, the constructional
tasks which also require motor ability and speeded responding in the performance scale –
due to their complexity – may be tapping an ability which is truly selectively lowered in
children with epilepsy and may be interfering with everyday functioning, more so than
more “pure” underlying neuropsychological functions. In this sense, one should consider
the possibility that the traditional measure may still be providing some kind of
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information than the narrower, more “neuropsychological function”-like index scores no
longer provide. It has been suggested that while these narrower factors intend to measure
specific abilities, they may not be doing so in a sufficiently diversified way (Grégoire,
2013). Of interest in the present context, for example, the processing speed index is
limited to visual-motor speed, not speed of auditory processing (Grégoire, 2013).
Therefore, the finding that PSI is lowered in epilepsy, still does not provide information
as to whether auditory-verbal processing speed may also be compromised. A similar kind
of “loss” has been has been noted by clinical psychologists regarding all revisions of the
Wechsler scales following David Wechsler’s death in 1981: the elimination of clinically
rich items or even an entire subtest (Baron, 2005; Kaufman, 1994).
Classification of Epilepsy. New efforts toward a different conceptualization and
classification of epilepsy are being undertaken (Berg et al., 2010; Berg & Cross, 2010;
Berg & Scheffer, 2011). Indeed, the traditional classifications have been qualified as
“both antiquated and arbitrary” (Berg & Scheffer, 2011, p.1058). The new classification
system kept the dichotomy focal versus generalized seizures (but no longer focal versus
generalized epilepsies). The newly suggested aetiological classification acknowledges
genetic causes, structural, metabolic, or immunological causes and unknown causes. It
also acknowledges that in a single child more than one of these causes may be at stake,
like genetic and structural. A flexible approach depending on the individual needs is
encouraged. The studies presented in this thesis relied on the “traditional” classification
of seizures and epilepsies (Engel, 2006; I.L.A.E., 1981, 1989), which continued to being
used in The Netherlands. Actually, in The Netherlands, new guidelines for epilepsy were
launched recently, developed by the Dutch League Against Epilepsy (LIGA, 2013) on
behalf of the Dutch Society of Neurologists and based on this traditional classification.
The Dutch League decided to hold on to the traditional classification, while at the same
time acknowledging the existence of a revised classification and the utility of adding
information according to this new classification (Augustijn, 2014). Interestingly, results
presented in Chapter 5 of the present work, showing that progressive cognitive decline in
epilepsy can be seen regardless of epilepsy variables, are congruent with propositions of
the new classification which indicate that encephalopathic effects (i.e., cognitive decline)
may be seen in any form of epilepsy (Berg & Cross, 2010).
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Future Directions
New developments. Special mention should be made of the rapid progress seen in
the field of epilepsy genetics (Addis et al., 2013; Koepp et al., 2014; Olson, Poduri, &
Pearl, 2014; Rudolf et al., 2009; Thomas & Berkovic, 2014) which undoubtedly will shed
new and possibly more decisive light on the results of the present papers. As mentioned in
Chapter 5, except for age at onset and duration of epilepsy, epilepsy variables did not
exert a significant contribution on the cognitive decline seen in epilepsy. Epilepsy
syndrome severity, for example, was not related significantly to cognitive decline. These
results suggest that some crucial information is still missing in the explanatory models.
New advances in genetics could be providing this key information as to why one child
previously diagnosed with a mild epileptic syndrome (e.g., absences or localization
related seizures) may develop in the expected manner, while another child will show
cognitive loss. The child showing cognitive decline may in fact have a genetic
abnormality or a pattern of abnormalities related to this cognitive loss.
The VIQ > PIQ difference as an early marker of a vulnerable brain. The
VIQ > PIQ pattern found in the previous chapters appears early in the course of epilepsy
(Chapter 5 and 6) and is largely specific for children with epilepsy. It is conceivable that
VIQ > PIQ could be a marker of the vulnerability of the brain to the underlying epileptic
condition which is about to emerge. It is proposed that this pattern may antedate the onset
of epilepsy. To examine this hypothesis, cognitive data of children before the onset of
epilepsy is of great value. One way of generating such data is the epidemiological study,
like the study conducted by Ellenberg et al. (1986). In the study of Ellenberg et al., from
45.000 children, 62 new cases with epilepsy had emerged between the first test at age 4
and the second test at age 7. A “not normal” status at age 4 was reported in 21% of these
children. Such a not normal status or a developmental problem may lead to testing before
the onset of epilepsy. Therefore, a second way of obtaining these data is to retrospectively
collect the data from children evaluated before the onset of epilepsy. The present studies
were limited to children assessed after the diagnosis of epilepsy was confirmed, but data
from children assessed for developmental problems before the emergence of epilepsy are
being collected as well. These data could be useful to test hypotheses associated with
cognitive patterns antedating epilepsy onset. Also, the cognitive status of children before
the onset of epilepsy could be studied in children who are at high developmental (e.g.,
genetic) risk for developing seizures.
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Specific samples. Further research on patterns of cognitive development in
children with epilepsy could include specific samples, like children with night-time
epileptic activity and children who have had status epilepticus. Two clinical observations
on exceptional cases may generate new studies. First, the VIQ > PIQ pattern
characteristic of children with epilepsy was not observed clinically in the small subset of
children with epilepsy born to mothers with epilepsy and prenatally exposed to anti-
epileptic drugs. Earlier studies have suggested that lowered verbal abilities may be
characteristic of children who were exposed to AEDs in utero (Meador et al., 2012).
Further research should be conducted to elucidate whether the VIQ > PIQ pattern so often
seen in children with epilepsy is reversed in children born to mothers with epilepsy.
Second, elevated rates of mortality in childhood-onset epilepsy have been reported
in a long-term follow-up study (Sillanpaa & Shinnar, 2010). Risk factors associated with
mortality were a severe cognitive impairment, symptomatic cause and active seizures for
which no medication was taken. On an observational level, during the period of data
collection, (sudden) death in epilepsy has been reported in some of the children who
presented with reliable cognitive decline. This raises the question whether cognitive
decline may be a marker of an increased vulnerability of the child or youngster to
(sudden) death.
Epilepsy and autism. Chapter 2 revealed overlap between autism spectrum
disorders and children with left hemisphere seizures: both samples showed increased
intra-individual variability on the verbal and full scales. The sample with autistic
spectrum disorders showed increased variability on the performance scale as well. This
result may be of particular relevance given the increasing interest in the co-occurrence of
autism spectrum disorders and epilepsy (Berg & Plioplys, 2012; Besag, 2009; Tuchman,
Alessandri, & Cuccaro, 2010). The overlap between the two conditions is suggested as
being as high as 15 to 30% (Russ et al., 2012; Tuchman et al., 2010). The overlap is seen
particularly in children with intellectual disabilities and in epileptic encephalopathies
(Besag, 2009; Tuchman et al., 2010). Some authors (Berg & Plioplys, 2012) point out that
it is still unclear whether the overlap is mediated by intellectual disabilities or whether it
is also seen in children with normal intellectual abilities. The results described in the
present study suggest a shared feature – increased verbal subtest scatter – in children with
close to average IQs (FS IQ ~ 92). Further research in this area of increased variability
may aid in the understanding of mechanisms shared by both conditions. For example,
increased variability in ASD has been associated with the lowered problems with the
DISCUSSION
129
integration of information (or deceased central coherence) in the ASD literature (Joseph,
Tager-Flusberg, & Lord, 2002; Noens & van Berckelaer-Onnes, 2005). Further research
could be aimed at studying whether these concepts would also be helpful in describing
children with epilepsy. In addition, increased variability within tasks has also been
described in ASD (Geurts et al., 2008). Variability within tasks has been associated with
abnormal patterns of brain development (Towgood, Meuwese, Gilbert, Turner, &
Burgess, 2009) as well as with deficits of working memory (Kofler et al., 2014). Of
interest, no differences emerged in children with isolated epilepsy and epilepsy with
comorbid ASD in terms of cognitive patterns (Chapter 6), again suggesting similarities
between the two conditions which would warrant future research.
Relevance of the Results
Beyond the significance of the results already indicated, some additional aspects
should be highlighted which may be of interest for researchers and clinicians.
Cognitive change over time and first and later testing. The present papers
suggest that cognitive data in children with epilepsy change over time. For research, one
implication of this finding is that the samples described in papers should be well
delineated. Information on age at testing, age at seizure onset and information on duration
of epilepsy is essential. It is also important to know whether earlier clinical assessment
took place before or after the onset of epilepsy, whether the test reported is the first or a
later test, whether assessments have occurred outside the research setting, whether the
interval between tests is long enough to preclude practice effects, whether all subjects
were tested with the same test version, and how all of this information is being dealt with.
To date, this information is often insufficiently accounted for in the literature.
Intra-individual subtest variability. Two opposing problems emerge relative to
intra-individual subtest variability. The first relates to ignoring variability at the time of
using short forms of the WISC in research; the second relates to the overinterpretation of
intra-individual subtest variability in clinical practice.
The use of short forms. Researchers on epilepsy often limit their Wechsler testing
to short forms (Bailet & Turk, 2000; Berg et al., 2008a, 2008b; Gülgönen,
Demirbilek, Korkmaz, Dervent, & Townes, 2000). One of the criteria in the use of a
short form is that 81% or more of the estimates of a scale fall within the 90% confidence
interval of the full-length scale (Donders, Elzinga, Kuipers, Helder, & Crawford, 2012),
but no efforts are done by the authors to ensure that this criterion is met in the sample
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
130
with epilepsy. Chapters 2 and 3 indicated that elevated subtest scatter can be found in
specific subsamples, suggesting a differential impact depending on seizure onset side and
presence of MRI-abnormalities on the verbal scale. If replicated with other samples, a
consequence of this finding is that the use of short forms in WISC testing, either for
clinical or research purposes (particularly on lateralized epilepsy and surgical studies),
should be applied with great caution. The best practice is to avoid the use of short forms
and administer all subtests. If this is not possible, both clinicians and researchers should
be aware that in particular samples, a short form is more likely to yield estimation errors.
Overinterpretation. In The Netherlands, in clinical practice, some clinicians
refrain from calculating and reporting IQ values in their psychological reports, arguing
that the variability seen between subtests is too large while, in fact, the observed
variability is normal variability. Already in the 1970s, Kaufman (1976) warned for this
overinterpretation of normal subtest variability, and presented base rates tables for subtest
scatter (Kaufman, 1979). The values given in Chapters 2 and 3 may help Dutch clinicians
to base their conclusions in concert with the psychometrically valid cut-off values for
Dutch data. In addition, base rate tables are given in the Appendixes to provide the
clinician with empirical information.
Epilepsy, comorbidities and cognitive patterns. The impact of the comorbidities
on cognitive patterns in children with epilepsy as seen in Chapter 6 and described in the
literature (B. Hermann et al., 2008) suggest that information on comorbidities is relevant
in studies on epilepsy. This is true regardless of whether the starting point is epilepsy (and
its learning and behavioural comorbidities) of neurodevelopmental disorders (and
comorbid epilepsy).
Importantly, an impact of the epilepsy on the cognitive pattern was seen, similar
across conditions. In all cases, the performance strength tended to disappear or a
performance weakness tended to increase in the light of epilepsy. These changes in
patterns in the light of comorbidities are relevant for the clinician. When testing children
for comorbidities like specific learning disorders or with behavioural conditions, the
clinician should be aware that the pattern seen in the disorder may be different from the
pattern seen in the isolated condition, and that the remediation measures should be
adapted as well. Thorough neuropsychological evaluation of the comorbidity is
recommended.
The possibility that reliable cognitive decline has occurred in epilepsy (Chapter 4)
should also be borne in mind whenever testing children for comorbidities. In educational
DISCUSSION
131
practice, “insufficient response to intervention” may be seen in a child. This lack of
scholastic progress may lead to referral questions on “specific learning disabilities”. It
may indeed be the case that the child has the learning comorbidity. In these cases, the
second diagnosis is warranted. The clinician should ensure, however, that insufficient
school progress is not, in fact, epilepsy-related “stagnation of development”, that is,
reliable cognitive decline, which is observed at school as difficulties to progress on school
subjects. Retesting the child for its cognitive abilities and comparing the scores with
earlier scores is recommended before diagnosing specific learning disabilities. As found
in Chapters 4 and 5, cognitive decline can occur in any child with epilepsy.
Cognitive change over time and educational measures. The findings on
cognitive decline at group level and reliable cognitive decline on the verbal scale in as
many as one out of four children, have significant implications for the school career and
the educational facilities for the children with epilepsy. The measures should be tailored
to suit the needs of the individual child. This means remediation for the lowered
performance abilities at baseline, adaptation of the curriculum to the specific difficulties
in terms of speed and visual, spatial and motor abilities. Also, advantage should be taken
of the assets of the child, like the higher verbal abilities seen in many children, both in
order to support the performance abilities as well as to keep standards high and try to hold
decline. It also means searching for specific ways to monitor development and academic
achievement and adapt the curricular demands to the new level of the child. It also may
mean psycho-social coaching of the child and the parents to enable adaptation to the new
level. Most of all, the findings should encourage the continuation of the educational
measures. Follow-up assessments and help for children with epilepsy should be offered
well beyond seizure remission. Beyond educational facilities, the finding of reliable
cognitive decline in children should alert clinicians to continue searching for causes and
treatment possibilities.
Clinical Data: The Appendices
As said in the Introduction, research should also be “consumer friendly”. All
chapters, except Chapter 5 (and Chapter 6, for which they are provided in Appendix E),
provide some information on individuals: frequency of occurrence, sensitivity, specificity
or results from Receiver Operating Characteristics. In addition, the Appendices provide
the clinician with base rate tables on intra-individual subtest variability (scatter; Appendix
A), verbal – performance discrepancies (Appendix B), discrepancies between factor index
scores (Appendix C) and cognitive change after serial testing (Appendix D).
Summaries
SUMMARY
135
Summary
(English Summary)
Cognitive Patterns in Paediatric Epilepsy
Intra-individual variability, cognitive patterns and patterns of cognitive change in children
with epilepsy on the Wechsler Intelligence Scales for Children
The present work includes five studies on the cognitive patterns in children with epilepsy.
This work is based on clinical data. The testing of children with various kinds of
disorders during a protracted period of time suggests that children with epilepsy displayed
patterns of cognitive abilities that differed from the patterns seen in other disorders. Such
clinical experience has sometimes been called the “internal database” of the clinician.
However, to be of true value, the internal data base should be confirmed with an
“empirical database”. The aim of the present study was to describe cognitive patterns in
children with epilepsy who were tested with the Wechsler Intelligence scales for children
(WISC series).
There is a large body of evidence that cognitive problems exist in children with
epilepsy. These problems also include the verbal and non-verbal (performance) abilities
of a child with epilepsy. Verbal and performance abilities are core abilities that are
measured in the intelligence scales for children as Verbal IQ and Performance IQ,
abbreviated as VIQ and PIQ, respectively. They are sampled in a standardized, well
normed and internationally widely accepted manner in the WISC series. Verbal and
performance abilities are partly independent of each other, but they also show a
substantial correlation, suggesting that they are both measures of the general factor IQ.
The verbal and performance IQ scales are comprised of subtests. The subtests are also
partly independent of each other but correlate with each other as they are all understood
be a measure of, for example, the verbal abilities.
Verbal and performance abilities are also the core abilities investigated in the
present work. While well researched, relatively less is known about the cognitive patterns
displayed on the verbal and performance scales by children with epilepsy. Cognitive
patterns relate to profiles of strengths and weaknesses and are, therefore, measures of
variability.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
136
One kind of variability is intra-individual subtest variability or subtest scatter. In
the literature – also on disorders other than epilepsy – it is still a matter of debate whether
increased intra-individual subtest variability is a sign of some kind of pathology or
whether it should be considered an expression of the statistical properties of the test. The
discussion remains important, because, for example in The Netherlands, it is common for
clinicians – also in the field of epilepsy – to say that the pattern displayed by a child
shows too much variability, and therefore does not provide a reliable measure of the
child’s abilities. They refrain from reporting IQ. In fact, for the Dutch Wechsler tests it is
largely unknown what can be considered normal scatter, and whether unusual scatter may
have any clinical diagnostic value in a child with epilepsy.
A second kind of variability relates to the pattern displayed by the verbal and
performance scales (VIQ and PIQ). A verbal – performance discrepancy suggests that a
particular domain is more compromised than the other. Existing studies lead to
conflicting results – even within a single epilepsy syndrome – as to which scale is most
lowered in epilepsy. Little is known about the differences in cognitive patterns between
children with epilepsy and other developmental disorders. Even less is known about
cognitive patterns in children affected by two conditions, that is, children with
comorbidities in epilepsy.
A third kind of variability relates to the changes over time that may occur in
children with epilepsy. Children with epilepsy often have seizures during a prolonged
period of time. During this period, they are expected to develop, while intermittent
epileptic discharges hamper cognitive functioning. The changes over time that may be
seen on the verbal and the performance scales, the possible differences in these changes
across the various cognitive domains, and the variables that affect these changes, are still
largely unknown.
The principal research questions of the present work relate to measures of cognitive
patterns or intra-individual variability. Do children with epilepsy present with more intra-
individual subtest variability than expected from the psychometric properties of the test?
Do children with epilepsy show lower scores on a particular measure and higher on
another? Do they show changes over time after serial testing? Do patterns change over
time?
SUMMARY
137
If increased intra-individual subtest variability can be found, particular cognitive
patterns or patterns of cognitive change over time – can variables be identified, associated
with these patterns?
The samples that participated in the different studies were referred children with epilepsy
who were tested because concerns had risen about their cognitive development. They
came to a tertiary epilepsy centre in The Netherlands or its associated school for epilepsy.
The school provided educational services, not only in the special school for epilepsy
proper, but in any primary or secondary school for regular or special education in the
northern half of the country. For clinical comparison, some samples of children with other
disorders (learning disorders, behavioural disorders) were included, as well as typically
developing children for whom no concerns about their development were known.
For all children, WISC data were collected. They related to the two most recent test
versions in The Netherlands, the WISC-RNL and WISC-IIINL, and sometimes to the WPPSI-RNL
as well (the superscript was applied to all Dutch test versions). For children with epilepsy,
additional data were collected from medical or neuropsychological reports. These data
concerned epilepsy variables, like age at onset of the epilepsy, type of seizures (focal
versus generalized), lateralization (left versus right hemisphere onset seizures), the
topographical localization of the seizures (e.g., frontal or temporal), the number of
different anti-epileptic drugs tried during the course of the epilepsy, and the presence of
brain lesions visible on MRI. Based on these data, other information could be extracted
such as the duration of the epilepsy or the severity of the epilepsy syndrome. Data on
participation in regular or special education and data on parental education were collected
as well.
After a general introduction, Chapter 2 studied intra-individual subtest variability, that is,
differences between the lowest and the highest subtest score. Based on 467 children with
developmental disorders (157 with epilepsy, 132 with learning disorders, 178 with
behavioural and psychiatric disorders) it was shown that scatter, while indeed somewhat
elevated in referred children, was not a general sign of pathology. Rather, differences
emerged depending on the sample studied. Children with epilepsy, and children with
learning disorders, did not display elevated subtest scatter. Elevated scatter was seen in
children with behavioural and psychiatric disorders (especially in children with autism
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
138
spectrum disorders.) Within the sample with epilepsy, increased variability appeared to be
dependent on lateralization: children with left hemisphere seizure activity showed
increased variability, not seen in children with right hemisphere seizures.
Chapter 3 studied subtest scatter in lateralized epilepsy in relation to brain lesions.
The study included 90 children with lateralized epilepsy, of whom 56 had epilepsy
emanating from the left hemisphere (LH; 22 of these children had shown abnormalities
on neuroimaging) and 34 had epilepsy from the right hemisphere (RH; 15 had MRI
abnormalities).It was found that there was a differential impact of side and lesion on
subtest scatter. Children with LH seizures and MRI lesions, displayed increased subtests
scatter; children with RH seizures and MRI lesions, displayed decreased scatter. In the
general Discussion it was speculated that in children with LH seizures and brain lesions,
reorganization of brain functions may lead to preservation of verbal abilities at the
expense of more scatter.
Chapter 4 studied the rate of children who showed reliable cognitive change at
retesting. Reliable cognitive change is a change in scores that is seen in less than 10% of
the children of a reference sample (5% should show gains, 5% losses). The cut-off values
were estimated based on a reference sample of referred children with developmental
disorders but without epilepsy. The data came from the literature, and were based on
children tested twice with Dutch versions of the WISC. Based on 73 children with epilepsy
tested two times with the same test version, after a mean interval of 2.3 years between test
and retest, 26% showed reliable cognitive change – as decline – on the verbal scale and
16.4% on the full scale. No increase in decline was seen on the performance scale in this
sample. Some children also showed reliable cognitive gains. The rate of children showing
gains, however, never surpassed the expected 5%.
Chapter 4 had suggested that there was an increased risk in epilepsy to show
cognitive decline over time. In Chapter 5, the variables associated with this decline were
studied. Based on 113 children tested two or three times with the Wechsler scales, decline
could once again be seen. The following variables did not contribute significantly to the
patterns of change: Hemispheric side or site of seizure onset, number of anti-epileptic
drugs tried over time, brain lesions, severity of the epileptic syndrome, or the actual
presence of seizures. In addition, interactions between these factors did not contribute to
the patterns change. The results were interpreted in the light of recent epilepsy studies,
which described lasting changes in the brain – for example in brain connectivity – in
areas distant from the sites of seizure onset, and lasting beyond seizure remission.
SUMMARY
139
The Chapter 5 study revealed interesting insights into the patterns of decline. A
lowered performance score could already be seen early in the course of the epilepsy,
while the verbal scale appeared relatively “spared.” Throughout the years, however, this
pattern changed. The verbal scale started to decline steeply; the performance scale also
declined, but less strongly; and the relative advantage of the verbal scale over the
performance scale could no longer be seen. The cognitive pattern changed over time as a
function of duration of epilepsy. At first, a steep decline could be seen, followed by a less
pronounced decline, which continued throughout several years. The result was a closing
of the VIQ > PIQ gap over time.
In addition to the variable “duration of epilepsy”, a second time-related variable
was found to contribute to the pattern: age at onset of epilepsy. Epilepsy starting early in
life was associated with a relatively higher VIQ at first testing and a relatively steeper
decline afterwards. Overall, there was large variability between children in terms of
cognitive patterns and patterns of decline.
Lowering of IQ scores could also be seen regardless of the school environment
(regular or special education), and regardless of the home environment (higher or lower
educated parents). As expected, IQ was higher in regular education relative to special
education, but decline was independent of school type. Higher parental educational level
was associated with higher IQ-scores in children, but was not “protective” against
decline.
The VIQ > PIQ pattern is not always seen in samples of children with epilepsy.
When epilepsy and a second disorder came together, the patterns appeared different.
Chapter 6 studied the cognitive patterns of 117 children with isolated disorders, including
epilepsy, but also reading disorders, math disorders and autism spectrum disorders. The
term “isolated” was applied to indicate that the children, while they might have had other
neuropsychological problems, did not have a diagnosis of a second disorder. This study
indicated that the children with epilepsy had a VIQ > PIQ pattern, while the other groups
had either a flat pattern of abilities or a pattern favouring the performance scale (most
clearly in reading disorders). Control children also had a flat pattern of abilities. In
addition, we included 171 children with epilepsy, of whom some had epilepsy as an
isolated condition, and others as a comorbid condition. The comorbidities were – again –
reading disorders, math disorders or autism spectrum disorders. Results of this study
indicated that the VIQ > PIQ pattern, characteristic for isolated epilepsy, was different in
the presence of a comorbidity. With comorbid reading disorders, the pattern appeared
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
140
attenuated; with comorbid math disorders, the pattern appeared exacerbated, with
comorbid autism spectrum disorders, no clear pattern emerged. Interestingly, when the
“other” disorder was taken as a starting point, and epilepsy co-occurred, there seemed to
be a systematic “shift” towards more compromised performance abilities or more spared
verbal abilities
Overall, a pattern of cognitive abilities and a pattern of cognitive changes emerged. In the
initial stages of the seizure condition, a pattern of relatively “spared” verbal abilities and
relatively “compromised” performance abilities is seen. The verbal abilities may be a
better indicator of the premorbid cognitive potential of the child. The performance
abilities may be an indicator of the vulnerable reaction of the brain to the seizure
condition. In some subsets of children, such as children with left hemisphere onset
seizures and brain lesions, the spared verbal abilities may appear with increased scatter on
the verbal scale. With increased duration of the epilepsy, the VIQ > PIQ pattern is likely
to change: the verbal scale starts to decline, steeply at the beginning, and more gradually
thereafter. The performance scale also declines further. Over time, the VIQ > PIQ gap is
no longer seen. The great majority of the children with epilepsy maintain a cognitive level
within a boundary wherein lowering of scores was seen, but not “reliable cognitive
change.” However, the percentage of children who showed such reliable change – as loss
– was elevated. This threefold decline on the full scale (and fivefold decline on the verbal
scale) often means that the children will fail to follow the educational trajectory they were
initially were enrolled in. They repeat grades, need special educational assistance, or, if in
secondary education, are set back to a lower type of education. Conceivably, these pattern
of change over time requires the children, the parents and the teachers to adapt to the new
situation. The VIQ > PIQ pattern seen in epilepsy appears modulated by two variables:
first, the duration of epilepsy which leads to the closing of the VIQ – PIQ gap. Second,
the presence of comorbidities which may decrease or augment the VIQ > PIQ difference.
The result of these studies lead us to hypothesize that lowered performance
abilities could be a cognitive marker of the vulnerability of the brain to the epileptic
condition, and it would be valuable to study whether this vulnerability can already be
seen before the emergence of the seizures proper.
The results of the different studies from the present work may be of utility for researchers,
policy makers and clinicians. For researchers, they results highlight that information on
SUMMARY
141
age at onset and duration of epilepsy, as well as on the presence of comorbidities, is
important when describing samples with epilepsy. In addition, the use of short forms
should be discouraged, given that subsets of children with epilepsy may show increased
subtest scatter and lead to increased measurement error. Wherever short forms cannot be
avoided for practical reasons, they should be tested for their utility in epilepsy.
Policy makers in education should take note that the cognitive trajectory seen in
children with epilepsy may be unlike the trajectory seen in other children with
developmental disorders. Children with epilepsy may therefore require specific expertise
from specialized school services, also beyond seizure remission.
When testing children with epilepsy, the clinician should be aware that children
with epilepsy may have a particular pattern of cognitive abilities, often VIQ > PIQ, and
that this pattern need not be maintained over time. Rather, it is likely to change over time.
When the child is diagnosed with a second condition (learning disorders, autism), the
clinician should keep in mind that the pattern displayed by the child may be different
from both the pattern characteristic for epilepsy as well as the pattern characteristic for
the other condition. The clinician should also bear in mind, that failure to progress in
school need not point to a specific learning disorder. Rather, it may point towards
cognitive decline (and therefore stagnation of school development), which may be much
more frequent in epilepsy than in other disorders without epilepsy. Increased intra-
individual subtest variability may be seen in some samples with epilepsy, and may prove
to have clinical value.
Finally, to make the results of the work “consumer friendly”, the appendices provide base
rate tables for children tested with various Dutch WISC test versions, (a) on subtest scatter;
(b) on discrepancies between scales and factor index scores; and (c) on change in IQ
scores over time. These tables are based on large numbers of clinically referred and non-
referred samples.
SAMENVATTING
143
Samenvatting
(Dutch Summary)
Cognitieve patronen in kinderen met epilepsie
Intra-individuele variabiliteit, cognitieve patronen en patronen van cognitieve verandering
in kinderen met epilepsie op de Wechsler Intelligence Scale for Children
Dit proefschrift bevat een vijftal studies over cognitieve patronen (cognitieve profielen)
bij kinderen met epilepsie.
De oorsprong van dit werk is klinisch van aard. Gedurende vele jaren van klinisch
neuropsychologisch werk met kinderen met allerlei verschillende
ontwikkelingsstoornissen viel op dat bij kinderen met epilepsie zich specifieke profielen
aftekenden, die verschilden van de profielen bij andere stoornissen. Klinische
opgebouwde ervaring is wel eens “de interne database” genoemd van de clinicus. Mooi
en belangrijk, maar pas werkelijk betekenisvol wanneer deze gestaafd kan worden aan
een “empirische database”. Het doel van dit onderzoek was om de patronen te beschrijven
bij kinderen met epilepsie die getest waren met de Wechsler Intelligentietest voor
kinderen (WISC).
In de wetenschappelijke literatuur is uitvoerig gedocumenteerd dat bij kinderen
met epilepsie vaak sprake is van cognitieve stoornissen. Tot de cognitieve vaardigheden
waarbij lagere scores worden gevonden, behoren in ieder geval ook de verbale en de niet-
verbale (performale) vaardigheden. Verbale en performale vaardigheden vormen de
hoofddimensies die door de intelligentietesten in kaart worden gebracht. Op een
gestandaardiseerde, goed genormeerde en internationaal erkende wijze gebeurt dat met de
verbale schaal (VIQ) en performale schaal (PIQ) van de Wechsler intelligentietests. De
verbale en de performale schaal zijn onderverdeeld in subtests. De verbale en performale
schalen zijn gedeeltelijk onafhankelijk van elkaar, maar vertonen ook een sterke
samenhang omdat ze beide de algemene vaardigheid weerspiegelen (totaal IQ of FS-IQ).
Iets vergelijkbaars kan gezegd worden van de subtests binnen een schaal: ze zijn
gedeeltelijk onafhankelijk, maar weerspiegelen allemaal de onderliggende vaardigheid, zo
weerspiegelen de verbale subtests het verbaal IQ.
Verbale en performale vaardigheden vormen ook de kern van het huidige werk. Er
is nog onvoldoende bekend welke relatieve sterktes en zwaktes op deze vaardigheden te
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
144
zien zijn bij kinderen met epilepsie. Het profiel van sterktes en zwaktes binnen het
testprofiel wordt hier aangeduid met “cognitieve patronen”, en heeft betrekking op de
variabiliteit van de testscores.
Een van de vormen van variabiliteit is de intra-individuele subtestvariabiliteit op
een schaal. Een subtestprofiel kan vlak zijn maar ook grillig – deze subtestvariabiliteit
wordt in de Engelstalige literatuur “subtest scatter” genoemd. Nog steeds is in de
literatuur – ook buiten de epilepsieliteratuur – discussie over over de vraag of verhoogde
intra-individuele variabiliteit een kenmerk is van een “stoornis”, of slechts een uiting is
van normale variatie op een test (zoals verwacht gezien de psychometrische
eigenschappen van de test). Toch is het belangrijk om dit te weten omdat clinici – ook in
de epilepsie – er snel toe neigen te spreken van een te grillig patroon van vaardigheden,
dat “het zicht op de werkelijke cognitieve vaardigheden belemmert”, en ertoe over gaan
het IQ niet te rapporteren. Dit doen ze zonder dat er voor de Nederlandstalige tests
bekend is, welke kritieke waarden nodig zijn om te kunnen spreken van zeldzaam hoge
subtestvariabiliteit (variabiliteit die bij minder dan 5% van de kinderen van een
normgroep voorkomt). Ook is niet bekend of verhoogde variabiliteit mogelijk van
klinische betekenis is bij het kind met epilepsie.
Een tweede type variabiliteit is het cognitief patroon of cognitief profiel dat zich
aftekent tussen de verbale en de performale vaardigheden, de verbaal – performaal
discrepantie die ontstaat doordat een cognitief gebied bij een kind met epilepsie relatief
sterker is aangedaan. Bestaande onderzoeken spreken elkaar tegen als het gaat om welke
schaalscore het meest verlaagd is bij kinderen met epilepsie, zelfs binnen één enkel
epilepsiesyndroom zijn er tegenstrijdige resultaten te zien. Er is weinig bekend over de
verschillen in cognitieve profielen tussen kinderen met epilepsie en kinderen en andere
stoornissen. Minder nog is er bekend over cognitieve profielen van kinderen met epilepsie
en een bijkomende stoornis, zogenaamde comorbiditeiten bij epilepsie.
Het derde type variabiliteit heeft betrekking op de mogelijke verschuivingen in IQ
die zich af kunnen spelen bij een kind met epilepsie door de tijd heen. Kinderen met
epilepsie hebben vaak gedurende verscheidene of zelfs vele jaren aanvallen. Ze moeten
zich ontwikkelen – doorgroeien – terwijl de epilepsie de hersenactiviteit met
onregelmatige tussenpozen verstoort. Over de mate waarin er in de loop der tijd
veranderingen optreden bij de verschillende cognitieve vaardigheden, of er verschillen
zijn in ontwikkelingsbeloop tussen de verschillende vaardigheden, en welke kenmerken
van epilepsie daarbij een rol spelen, is nog niet veel bekend.
SAMENVATTING
145
De belangrijkste vragen van deze verhandeling hebben betrekking op cognitieve patronen
van intra-individuele variabiliteit. Tonen profielen van kinderen met epilepsie meer
variabiliteit in testscores dan men op grond van de psychometrische eigenschappen van
de test zou verwachten? Gaat epilepsie samen met hoge scores op de ene taak of op het
ene meetmoment en lage op een andere taak of een ander meetmoment? Verandert dit
patroon in de loop van de tijd? Indien er sprake is van verhoogde intra-individuele
variabiliteit, van specifieke patronen, of van patronen van verandering door de tijd heen,
kunnen er variabelen worden geïdentificeerd die samenhangen met deze patronen?
De groep waarbij deze vragen werden bestudeerd bestond uit kinderen met epilepsie bij
wie vragen over hun cognitieve ontwikkeling waren gerezen. Ze waren aangemeld voor
neuropsychologisch onderzoek bij een tertiair centrum voor epilepsie, of de eraan
verbonden school voor kinderen met epilepsie. De school voor epilepsie biedt onderwijs
of onderwijsondersteuning aan kinderen met epilepsie. Niet alleen op de eigen school,
maar ook op iedere andere school voor regulier of speciaal primair of voortgezet
onderwijs in de noordelijke helft van het land. Ter vergelijking met de kinderen met
epilepsie, zijn in enkele studies ook kinderen met andere ontwikkelingsstoornissen
(leerstoornissen en gedragsstoornissen) opgenomen, evenals kinderen uit het
basisonderwijs bij wie er geen reden was tot zorg.
Bij alle kinderen werden WISC-gegevens verzameld. Het betrof daarbij de twee
meest recente versies van de Nederlandstalige WISC, de WISC-RNL of de WISC-IIINL, soms
ook de kleuterintelligentietest WPPSI-RNL (het superscript werd op alle Nederlandstalige
testversies toegepast). Bij kinderen met epilepsie werden daarnaast ook gegevens
verzameld die gerelateerd zijn aan de aard en de ernst van de epilepsie. Deze gegevens
kwamen van de medische of neuropsychologische rapporten en hadden betrekking op de
leeftijd bij aanvang van de epilepsie, de aard van de epilepsieaanvallen (focaal dan wel
generaliseerd), de lateralisatie van de epilepsie (uitgaande van de linker dan wel rechter
hersenhemisfeer), de topografische lokalisatie van de epilepsie (bijvoorbeeld frontaal,
temporaal), de aanwezigheid van hersenlaesies zichtbaar bij beeldvormend onderzoek
(MRI), het aantal typen medicijnen dat in de loop van de tijd was ingenomen. Aan de
hand van deze gegevens konden andere maten worden bepaald, zoals de duur van de
epilepsie op het moment dat de test werd afgenomen en de ernst van het
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
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epilepsiesyndroom. Ook werden gegevens omtrent de deelname aan het speciaal
onderwijs en het opleidingsniveau van de ouders verzameld.
Na de algemene inleiding werd in Hoofdstuk 2 onderzocht of er bij kinderen met
epilepsie sprake is van toegenomen intra-individuele subtestvariabiliteit (subtest scatter).
In de steekproef waren 467 kinderen opgenomen, van wie 157 epilepsie hadden, 132
gedrags- of psychiatrische stoornissen, en 178 leerstoornissen. Door ook kinderen met
andere stoornissen op te nemen kon bekeken worden of grotere variabiliteit een kenmerk
kon zijn voor ontwikkelingsstoornissen. Het resultaat van het onderzoek was dat
verhoogde variabiliteit, hoewel inderdaad in enige mate aanwezig, niet als een algeheel
kenmerk van kinderen met ontwikkelingsstoornissen kon worden beschouwd. Er waren
verschillen te zien afhankelijk van de onderzochte groep of subgroep. Kinderen met
epilepsie, evenals kinderen met leerstoornissen, lieten geen verhoogde subtestvariabiliteit
zien. Kinderen met gedragsstoornissen (en onder hen, vooral de kinderen met autisme
spectrum problematiek) toonden wel verhoogde variabiliteit. Ook bij subgroepen van
kinderen met epilepsie was het beeld genuanceerder. Kinderen met linker hemisfeer
epilepsie toonden verhoogde variabiliteit, met andere woorden een grillig profiel, terwijl
dit bij kinderen met epilepsie startende in de rechter hemisfeer niet te zien was.
In Hoofdstuk 3 werd nader ingegaan op de invloed van de lateralisatie en
aanwezigheid van hersenlasesies op subtestvariabiliteit. De onderzoeksgroep bestond uit
90 kinderen met gelateraliseerde epilepsie. Van deze kinderen hadden 56 linkszijdige
epilepsie, van hen hadden 22 tevens een op het MRI zichtbare hersenlaesie. Vierendertig
kinderen hadden rechtszijdige epilepsie; van hen had 15 een MRI-laesie. Ook nu werd de
subtest scatter onderzocht. Uit dit onderzoek kwam naar voren dat er een differentieel
effect was op variabiliteit, afhankelijk van de zijde en de aanwezigheid van een leasie.
Kinderen met laesies op een MRI en epilepsie uitgaande van de linker hemisfeer
vertoonden verhoogde variabiliteit; terwijl kinderen met laesies op een MRI en epilepsie
uitgaande van de rechter hemisfeer verminderde variabiliteit vertoonden. In de algemene
discussie werd gespeculeerd dat reorganisatie van hersenfuncties bij kinderen met linker
hemisfeer en hersenlesies mogelijk ten koste gaat van een verhoogde grilligheid in het
subtestpatroon.
In Hoofdstuk 4 werd bij kinderen met epilepsie bekeken hoe vaak er sprake was
van een klinisch betekenisvolle verandering in IQ. De groep bestond uit 73 kinderen die,
met een tijdsinterval van 2,3 jaar, twee keer met dezelfde WISC versie werden onderzocht.
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Het betrof twee keer de WISC-RNL, danwel de WISC-IIINL. Een verandering wordt als
klinisch betekenisvol begrepen wanneer deze zeldzaam is en bij een vergelijkingsgroep in
slechts 10% van de kinderen voorkomt, bij 5% als een stijging en bij 5% als een daling
van het IQ. Als referentie voor de berekening van de kritieke waarden werd gebruik
gemaakt van een Nederlandstalige vergelijkingsgroep uit de literatuur. De
referentiegegevens hadden betrekking op kinderen met ontwikkelingsproblemen zonder
epilepsie, die op vergelijkbare wijze twee keer waren getest, eveneens met een
tijdsinterval van ruim twee jaar. Bij deze vergelijking toonden we aan dat er bij kinderen
met epilepsie drie keer zo vaak een betekenisvolle daling te zien was op totale schaal
(namelijk bij 16.4% van de kinderen), en vijf keer zo vaak (26%) op het verbale schaal.
Op de performale schaal werden in deze tussenliggende periode van ruim twee jaar geen
veranderingen gezien. Er waren ook kinderen die een stijging van het IQ lieten zien; deze
stijgingen kwamen echter niet vaker voor dan de verwachte 5%.
De resultaten van Hoofdstuk 4 gaven aan dat er bij kinderen met epilepsie een
verhoogd risico is op een daling van het verbale en totale IQ ten aanzien van kinderen met
andere ontwikkelingsproblemen. In Hoofdstuk 5 werd onderzocht, welke variabelen
bijdragen tot IQ-dalingen. De steekproef bestond uit 113 kinderen met epilepsie die twee
of drie keer waren onderzocht met de WPPSI-RNL, de WISC-RNL of de WISC-IIINL. Ook nu
werd een daling in IQ gevonden. Epilepsie gerelateerde variabelen, zoals de zijde van de
epilepsie, de lokalisatie van de epilepsie, de aard van de epilepsieaanvallen, het aantal
verschillende medicijnen dat in de loop der tijd was ingenomen, de aanwezigheid van
laesies in het brein, de ernst van het epilepsiesyndroom, of het feit of de aanvallen onder
controle waren bij hertest met de WISC (al dan geen aanvalsvrijheid bij de laatste test),
leverden geen van alle een statistisch betekenisvolle bijdrage aan de daling in IQ. Dit
resultaat was opmerkelijk, maar kon wel worden geïnterpreteerd binnen het licht van
recente studies die aantoonden dat er bij epilepsie veranderingen plaatsvinden in
netwerken van het brein, ook ver van de plek waar de aanval ontstaat, en dat die
veranderingen blijven voortbestaan ook nadat de aanvallen onder controle zijn.
Opvallend was dat een lage score al te zien was bij de eerste testafname,
voornamelijk op de performale schaal, terwijl de verbale schaal bij de eerste meting
betrekkelijk “gespaard” leek te blijven. Door de tijd heen veranderde dit beeld evenwel.
De verbale schaal daalde in een versneld tempo, de performale schaal daalde ook maar
minder sterk, en na verloop van tijd was de relatieve voorsprong van de verbale schaal ten
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
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aanzien van de performale niet meer te zien. De variabele duur van de epilepsie bleek
daarmee een belangrijke variabele te zijn; de daling in de tijd had een logaritmische vorm.
Naast de variabele duur van de epilepsie, bleek ook een tweede aan tijd
gerelateerde variabele een bijdrage te leveren aan het IQ-patroon en de daling van de
verbale en de performale schaal. Het ging daarbij om de leeftijd van de aanvang van de
epilepsie. Een jongere leeftijd was gerelateerd aan een relatief hogere beginscore op de
verbale schaal, en een relatief sterkere daling van de verbale schaal door de tijd heen.
Bij dit onderzoek werd tevens gekeken naar de bijdrage van onderwijstype
(regulier of speciaal onderwijs) en van het opleidingsniveau van de ouders, aan het
intelligentieprofiel van het kind. Zoals verwacht, hadden kinderen die geplaatst waren in
het speciaal onderwijs, lagere IQ-scores dan kinderen in het reguliere onderwijs. Plaatsing
in het speciaal onderwijs had evenwel geen relatie met de daling van het IQ. Kinderen
van ouders met een hogere opleiding scoorden hoger; maar ook hier bleek dat een hoog
opleidingsniveau geen “beschermende” factor was tegen een daling van IQ. Anders
gezegd: zowel op het reguliere onderwijs als op het speciaal onderwijs konden dalingen
in IQ worden gezien, evenzeer bij kinderen van hoog opgeleide ouders als bij die van
minder hoog opgeleide ouders.
In Hoofdstuk 6 werden de discrepanties tussen de verbale en de performale
schaal opnieuw bekeken, nu in het licht van de aanwezigheid van comorbide stoornissen.
Een comorbiditeit is een tweede gediagnosticeerde aandoening, waarvan er een relatie
met de eerste aandoening wordt verondersteld, omdat de aandoening in combinatie vaker
voorkomt dan men op basis van toeval zou verwachten. Twee steekproeven werden in
deze studie opgenomen. De eerste steekproef bestond uit 117 kinderen die slechts één
diagnose hadden. Deze diagnose betrof epilepsie, specifieke leesstoornissen, specifieke
rekenstoornissen, dan wel autisme spectrum stoornissen. Deze kinderen werden met de
term “geïsoleerde” stoornissen aangeduid, hoewel ze natuurlijk ook verdere
neuropsychologische problemen konden hebben, die echter niet tot een tweede diagnose
hadden geleid. Het profiel dat de verbale en de performale schalen te zien gaven werd
vergeleken. Uit dit deelonderzoek kwam naar voren dat kinderen met epilepsie een profiel
van relatief betere verbale dan performale vaardigheden lieten zien (VIQ > PIQ), dat niet
getoond werd door de andere groepen. De andere groepen hadden een vlak profiel (zoals
ook controle kinderen zonder problemen) of een profiel waarbij de verbale vaardigheden
zwakker waren dan de performale (vooral de kinderen met leesstoornissen). In die zin
leek het VIQ > PIQ profiel vrij specifiek te zijn voor kinderen met epilepsie. De tweede
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steekproef bestond uit 171 kinderen met epilepsie. Ook hier waren kinderen opgenomen
met geïsoleerde epilepsie, maar ook kinderen met epilepsie en cormorbide
leesstoornissen, rekenstoornissen of autisme spectrum stoornissen. Opnieuw werden de
patronen van kinderen met geïsoleerde epilepsie vergeleken met de patronen van de
kinderen met leer- of gedragsstoornissen, nu als comorbide stoornissen. Hieruit kwam
naar voren, dat de voor kinderen met geïsoleerde epilepsie kenmerkende VIQ > PIQ
discrepantie, was “afgevlakt” als het kind naast epilepsie een leesstoornis had. De
discrepantie was versterkt te zien als het kind naast epilepsie een rekenstoornis had. Er
werden geen verschillen gezien tussen geïsoleerde epilepsie en epilepsie met autisme.
Geconcludeerd werd dat bij kinderen bij wie twee stoornissen bij elkaar kwamen –
epilepsie en nog een tweede stoornis – het voor epilepsie kenmerkende patroon van
verbale en performale vaardigheden veranderd was. Omgekeerd, gegeven de stoornis
zonder epilepsie, kon gezegd worden dat de “impact” die de epilepsie op het cognitieve
profiel had, bij alle beelden vergelijkbaar leek, namelijk een “systematische
verschuiving” in de richting van een lager performaal IQ danwel in de richting van een
meer gespaard verbaal IQ.
Samenvattend leidden deze resultaten tot een schets van de ontwikkeling van de
cognitieve vaardigheden van kinderen met epilepsie, die op deze wijze niet eerder in de
literatuur gemaakt was. Al gauw na de aanvang van de epilepsie, zijn cognitieve
achterstanden te zien. Deze zijn het best zichtbaar op de performale schaal. De verbale
schaal blijft op dat moment relatief gespaard. Een VIQ > PIQ patroon wordt zichtbaar,
waarbij de verbale score mogelijkerwijs een betere maat is van het oorspronkelijke
potentieel van het kind, en de performale score mogelijk een betere maat is van de
kwetsbare reactie van het brein op de epilepsie. Na verloop van tijd begint het IQ te dalen,
zowel het verbale als het performale IQ. De daling is het duidelijkst te zien in de eerste
(vijf) jaren na aanvang van de epilepsie, maar kan daarna nog lange tijd voortduren. De
daling is het sterkst op de verbale schaal, en na enige tijd wordt het verschil tussen de
verbale en de performale schaal niet meer gezien. De meeste kinderen laten bij een hertest
van ruim twee jaar een score zien die binnen de kritieke waarden valt. Het aandeel
kinderen, dat een klinisch zeldzame (en daarom als betekenisvol beschouwde) daling te
zien geeft op de verbale schaal of op de totale schaal, is evenwel verhoogd. Op de totale
schaal is deze drie keer zo hoog als de verwachte daling bij andere kinderen met
ontwikkelingsproblemen maar zonder epilepsie, en op de verbale schaal vijf keer zo hoog.
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Bij het langer duren van de epilepsie worden ook frequent dalingen op de performale
schaal gezien.
Het profiel dat kenmerkend is voor epilepsie, VIQ > PIQ, wordt door twee
belangrijke factoren gemoduleerd. De eerste is de duur van de epilepsie. Naarmate de
epilepsie langer duurt, verdwijnt het voordeel van de verbale boven de performale schaal.
De tweede is de aanwezigheid van comorbide problemen. Bij kinderen met
leesstoornissen én epilepsie wordt het VIQ > PIQ niet gezien, maar is er een vlak profiel;
bij kinderen met rekenstoornissen én epilepsie wordt het VIQ > PIQ profiel juist vergroot.
De klinische relevantie van deze resultaten kan op verschillende niveaus worden
besproken. Allereerst is het voor ouders, leerkrachten en onderwijsbegeleiders van belang
om te weten dat er bij kinderen met epilepsie sprake kan zijn van dalingen in cognitieve
vaardigheden, die niet op elk deelgebied even groot zijn. Met name de dalingen in het
verbale IQ zullen consequenties hebben voor de schoolcarrière. Doublures, teruggezet
worden naar een lagere vorm van (voortgezet) onderwijs, plaatsing in het speciaal
onderwijs behoren allemaal tot de mogelijkheden waarvoor een kind met epilepsie een
verhoogd risico heeft, en waarin het met zorg begeleid moet worden. Aanpassingen in het
onderwijs (zoals preteaching) zouden zo vroeg mogelijk ingezet moeten worden.
Kinderneurologen zouden alert moeten zijn op informatie die duidt op stagnaties.
Stagnatie zou een aanleiding moeten zijn voor nader onderzoek naar de cognitieve
ontwikkeling. Ook kan het een aanleiding zijn om op zoek te gaan naar de achterliggende
etiologie.
In de tweede plaats kunnen de resultaten betekenisvol zijn voor beleidsmakers in
het onderwijs. De onderzoeken tonen aan dat het ontwikkelingsbeloop van kinderen met
epilepsie verschilt van dat van kinderen met andere ontwikkelingsstoornissen. Zowel het
epilepsie-specifieke cognitieve profiel alsook het beloop door de tijd heen (cognitieve
achterstanden kunnen verergeren, ook nadat de aanvallen wegblijven) vragen aangepaste
voorzieningen en expertise gericht op kinderen met epilepsie.
Voor onderzoekers op het gebied van epilepsie is het van belang dat variabelen als
leeftijd bij aanvang van epilepsie, duur van epilepsie en aanwezigheid van comorbide
stoornissen, die alle van invloed zijn op het cognitieve profiel, opgenomen worden in de
beschrijving van de onderzochte steekproeven. Afhankelijk van de lateralisatie van de
epilepsie en van de integriteit van het brein, zal meer of minder variabiliteit in de
testscores te zien zijn. Toegenomen variabiliteit brengt met zich mee dat bij gebruik van
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verkorte testversies de kans op meetfouten toeneemt. Verkorte versies kunnen het beste
vermeden worden. Indien ze toch gebruikt worden, zouden ze op zijn minst getoetst
moeten worden op bruikbaarheid voor de onderzochte groepen.
Niet in de laatste plaats kunnen de resultaten van de verschillende onderzoeken
ook van waarde zijn voor de (neuro)psycholoog en orthopedagoog die diagnostisch werk
verricht in de klinische setting. De informatie kan een bijdrage leveren tot de beschrijving
en interpretatie van testresultaten en de klinische besluitvorming. (1) Pas wanneer
subtestvariabiliteit 8 punten of meer op de verbale schaal beslaat, 10 of meer op de
performale, en 11 of meer op de totale schaal, kan er gesproken van zeldzaam grote
variabiliteit. Deze zeldzaam grote variabiliteit kan klinische betekenis hebben. (2) Pas
wanneer bij hertest de daling in (WISC-RNL, WISC-IIINL) IQ 14 punten of meer op de verbale
schaal bedraagt, 18 of meer op de performale, en 14 of meer op de totale schaal, kan er
gesproken worden van een zeldzame en klinische betekenisvolle verandering in IQ. Bij
tussentijdse verandering van test van de WISC-RNL naar de WISC-IIINL zijn deze waarden
voor verlies aan vaardigheden 19, 18 en 17 IQ-punten. (3) Significante verbaal –
performaal verschillen hoeven niet te worden beschouwd als een reden om het totaal IQ
achterwege te laten. In plaats daarvan kunnen ze worden geïnterpreteerd als klinisch
relevant. De aanwezigheid van een comorbide stoornis kan samengaan met een ander
patroon van vaardigheden. Het profiel van een kind met een leesstoornis of rekenstoornis
kan er anders uit komen te zien wanneer er ook epilepsie in het spel is. De aard van het
verschil is telkens in de richting van een minder gespaarde performale schaal, of een
relatief beter behouden verbale schaal. (4) Tot slot worden in de Appendices “base rate”
tabellen weergegeven, gebaseerd op klinische data van verscheidene Nederlandstalige
WISCs. Deze base rate tabellen geven de frequentie van voorkomen van bepaalde groottes
van subtestvariabiliteit, discrepanties tussen schalen en factoren, en verschillen in IQ bij
een herstest zoals in de klinische setting waargenomen bij ruim duizend Nederlandse
kinderen.
RESUMEN
153
Resumen
(Spanish Summary)
Patrones cognitivos en epilepsia de niños con epilepsia.
Variabilidad intra-individual, perfiles cognitivos y patrones de cambio en niños con
epilepsia en las Escalas de Inteligencia Wechsler para niños
Este trabajo reúne cinco estudios sobre los perfiles cognitivos de niños y niñas con
epilepsia.
El origen del trabajo es clínico. El estudio diagnóstico clínico con niños con
distintos tipos de trastornos del desarrollo indicaba que los perfiles cognitivos de niños
con epilepsia se diferenciaban de los perfiles que se encontraban en otros desórdenes.
Este tipo de observaciones, que se han llamado “la base de datos clínica” (Baron, 2005)
del neuropsicólogo clínico, cobra importancia cuando se logra confirmar con una “base
de datos empírica”. El objetivo del presente estudio era, pues, describir los perfiles
cognitivos de niños con epilepsia evaluados con el instrumento de mayor uso en la
evaluación neuropsicológica de niños: las Escalas de Inteligencia de Wechsler para Niños
(el test de WISC).
Ya se ha estudiado ampliamente que niños con epilepsia corren el riesgo de tener
problemas cognitivos. Estudios existentes también incluyen datos sobre las habilidades
verbales y no verbales (llamadas de ejecución) del niño con epilepsia. Las habilidades
verbales y de ejecución son, tradicionalmente, las principales habilidades evaluadas por
los tests de inteligencia. Abreviadas como VIQ y PIQ en la literatura internacional (y
como cociente intelectual [CI] verbal y CI de ejecución en la de lengua española), forman
conjuntamente la escala total (FS-IQ o bien CI total).
Las habilidades verbales y de ejecución se evalúan de forma estandarizada,
escrupulosamente normada e internacionalmente aceptada con las varias escalas del
WISC. Las escalas verbal y de ejecución son escalas independientes, pero altamente
correlacionadas, sugiriendo que ambas hacen referencia a un constructo común, la
inteligencia general. Igualmente, las (sub)pruebas son independientes, pero se relacionan
entre sí y se relacionan con un constructo, por ejemplo cinco subpruebas verbales
conforman la escala verbal.
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154
Las habilidades verbales y de ejecución también forman el núcleo del presente
trabajo. Aunque se han estudiado bastante ya, se sabe relativamente menos sobre los
perfiles cognitivos en niños con epilepsia. El término perfil cognitivo – valores altos en
una medida y bajos en otra – se refiere a medidas de variabilidad dentro de un mismo
individuo.
Un tipo de variabilidad se refiere a la variabilidad intra-individual entre las
subpruebas, denominada subtest scatter en la literatura inglesa. En la literatura – también
fuera del ámbito de la epilepsia – aún se debate si la variabilidad entre subpruebas se ve
incrementada en trastornos, y, por ende, si se puede interpretar como algún tipo de
patología, o, si debe considerarse meramente una manifestación de las cualidades
psicométricas de la prueba.
La discusión se torna importante cuando se observa que psicólogos clínicos, por
ejemplo en el área de epilepsia en Holanda, indican que el perfil de un niño muestra una
alta variabilidad y que por ello la escala no es una buena medida de las habilidades del
examinado. Prosiguen a omitir el CI en el informe. Esto llama la atención porque para las
pruebas neerlandesas se desconoce cuáles son los valores críticos para poder hablar de
“variabilidad alta”, y si esa alta variabilidad tiene algún valor para el diagnóstico clínico.
Un segundo tipo de variabilidad en el perfil se refiere a la discrepancia entre la
escala verbal y la de ejecución (VIQ – PIQ). Una discrepancia alta entre la escala verbal
y de ejecución indicaría que un área se ve más comprometida que la otra. Los estudios
existentes – incluso dentro de un mismo síndrome epiléptico – se contradicen. Es poco lo
que se sabe sobre las diferencias entre los patrones cognitivos de niños con epilepsia y
niños con otros trastornos del desarrollo. Aún menos se sabe del perfil cognitivo del niño
que se ve afectado por dos condiciones a la vez, es decir, niños con comorbilidades de la
epilepsia.
Un tercer tipo de variabilidad se relaciona con los cambios en el curso del tiempo,
que pueden ocurrir en niños con epilepsia. Los niños con epilepsia suelen tener crisis
epilépticas durante un periodo prolongado. Durante este periodo, se espera de ellos que se
desarrollen, mientras que las crisis intermitentes interfieren con el funcionamiento
cognitivo. Los cambios a lo largo del tiempo que se puedan producir en las escalas de
inteligencia se desconocen en su mayoría. Igualmente, se ignora si los posibles cambios
que se puedan dar en el curso del tiempo difieren entre la escala verbal y la de ejecución.
Y de ser así, cuáles variables afectan estos cambios.
RESUMEN
155
La principal cuestión a tratar en los diversos estudios es el perfil cognitivo en
niños con epilepsia. Los temas a investigar se relacionan con la variabilidad dentro de un
mismo individuo: (1) la variabilidad entre las subpruebas, (2) la discrepancia entre la
escala verbal y la de ejecución (VIQ – PIQ), (3) las diferencias entre la primera
evaluación y la segunda en niños evaluados más de una vez.
En caso de encontrar incrementos en la variabilidad dentro del mismo individuo,
en la forma de perfiles o perfiles de cambio, surge la pregunta de si se pueden identificar
variables que se asocian con tales perfiles o perfiles de cambio.
Estos temas se estudiaron en muestras grandes de niños/as con epilepsia evaluados
porque existía preocupación sobre su desarrollo cognitivo. Se habían presentado a un
instituto de epilepsia neerlandés o a la escuela para niños con epilepsia asociada con el
instituto. Éste atiende a personas con epilepsia a nivel terciario y cubre la mitad
septentrional de los Países Bajos. La escuela atiende a niños con epilepsia tanto dentro de
su propio plantel como en cualquier escuela regular o especial, primaria o secundaria en
la mitad norte del país, siempre y cuando haya una indicación para ello. Además, a modo
de comparación, en algunos estudios se incluyeron muestras de niños con trastornos del
desarrollo de otra índole, tales como trastornos específicos de aprendizaje y trastornos
psiquiátricos y de conducta, al igual que niños sin trastornos.
Para todos los niños, se recogieron datos de las escalas del WISC neerlandesas
más recientes, el WISC-RNL y el WISC-IIINL, y en algunos casos también el WPPSI-RNL para
preescolares (el volado NL se aplicó a todas las pruebas neerlandesas). Para los niños con
epilepsia, se recolectaron datos adicionales de informes médicos y neuropsicológicos.
Estos se refieren a variables de epilepsia, tales como la edad del inicio de la epilepsia, el
tipo de crisis (parciales o generalizadas), la lateralidad (actividad epiléptica con origen en
el hemisferio izquierdo, LH, o derecho, RH), la localización (por ejemplo, del lóbulo
frontal o temporal), el número de medicamentos tomados en el curso del tiempo, la
presencia de lesiones cerebrales detectadas por resonancia magnética (RM). Con base en
estos datos, se extrajo información adicional tal como la duración de la epilepsia o la
severidad del síndrome epiléptico. Para los niños descritos en el Capítulo 6. Se recogieron
datos sobre comorbilidades. También se recogieron datos sobre participación en
educación regular o especial y datos sobre el nivel educativo de los padres.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
156
Luego de la Introducción general, en el Capítulo 2, y con base en 467 niños, se
estudió el tema de la variabilidad intra-indvidual entre las subpruebas. El estudio se basó
en 157 niños con epilepsia, 132 con trastornos del aprendizaje, y 178 con trastornos
psiquiátricos. Encontramos que la variabilidad se encuentra sólo levemente elevada en los
grupos clínicos en general, y en ese sentido no aparece como un indicador general de
patología, sino que depende del grupo diagnóstico estudiado. Los niños con epilepsia, al
igual que los niños con problemas de aprendizaje, no presentan una variabilidad elevada.
Los niños con trastornos psiquiátricos, y entre ellos ante todo los niños con trastornos en
el espectro autista, por otro lado, sí muestran variabilidad elevada. Dentro del grupo con
epilepsia, se percibió un incremento de la variabilidad en epilepsia de lateralidad
izquierda, pero no en epilepsia de lateralidad derecha.
El Capítulo 3 estudió la variabilidad intra-individual entre subpruebas en relación
con lesiones cerebrales. El estudio incluyó 90 niños con epilepsia lateralizada. De ellos,
56 niños tenían epilepsia que emanaba del hemisferio izquierdo (de ellos 22 tenían
lesiones en RM), y 34 tenían epilepsia del hemisferio derecho (15 con lesiones en RM).
Se encontró que la variabilidad entre las subpruebas se encuentra incrementada en
epilepsia de lateralidad izquierda, en especial en casos con lesiones cerebrales detectadas
en imágenes por resonancia magnética, mientras que en casos de lateralidad derecha y
lesiones, la variabilidad se encuentra disminuida. En la Discusión general se especula que
una posible reorganización cerebral, en el caso de lesiones del hemisferio izquierdo, se
produzca a favor de la conservación de las habilidades verbales pero a costa de un
incremento de la variabilidad.
En el Capítulo 4 se estableció el porcentaje de niños que presentaban cambios
confiables de inteligencia en la segunda evaluación con la misma prueba (bien fuera el
WISC-RNL o el WISC-IIINL) en 73 niños con epilepsia. Cambios confiables se definieron
como aquellos que se presentan en sólo 10% de los niños con trastornos de desarrollo
(pero sin epilepsia), en el 5% como incrementos en el cociente intelectual y en el 5%
como pérdidas (deterioro) del cociente intelectual. Encontramos que, en la escala verbal,
el porcentaje de niños con pérdidas era del 26, en la escala total, del 16.4, es decir cinco
veces más que lo esperado en la escala verbal y tres veces más en la total. En la escala de
ejecución, los cambios no eran diferentes a los esperados. Igualmente, algunos niños
presentaban incrementos en el cociente intelectual; estos porcentajes nunca superaron los
esperados.
RESUMEN
157
Dados estos resultados, en el Capítulo 5 estudiamos las variables que podían estar
influyendo en el deterioro de las habilidades cognitivas de los niños con epilepsia. Con
base en 113 niños evaluados dos o tres veces con las escalas de Wechsler, no se encontró
una contribución significativa para la mayoría de las variables de epilepsia. Así mismo,
no se encontró una contribución para la lateralidad, la localización, el tipo de crisis, el
número de fármacos antiepilépticos tomados en el curso de la epilepsia, la presencia de
lesiones en RM, la severidad del síndrome epiléptico, ni la presencia de crisis en la última
evaluación (libre o no libre de crisis). Tampoco se halló una contribución significativa
para la interacción de las variables. Estos resultados fueron interpretados a la luz de
estudios que han demostrado cambios cerebrales duraderos – por ejemplo en la
conectividad cerebral – en áreas distantes a aquellas en las que se origina la actividad
epiléptica; cambios que perduran aunque la epilepsia haya entrado en remisión.
El patrón de declive encontrado en el Capítulo 5 fue llamativo. Al comienzo, se ve
que la escala verbal supera la escala de ejecución. Luego, se aprecia un cambio que no se
describe con una curva lineal sino logarítmica. En los primeros años se ve un deterioro
pronunciado, posteriormente, el ritmo se desacelera, pero el descenso continúa por un
tiempo prolongado. Los cambios se ven en ambas escalas, pero es más pronunciado en la
escala verbal, por lo que, con el transcurso del tiempo, la ventaja inicial de la escala
verbal sobre la de ejecución tiende a desaparecer. El perfil cambia en el curso del tiempo
en función de la duración de la epilepsia.
Además de la variable “duración de la epilepsia”, se encontró otra, también
asociada con el factor tiempo, que contribuye al perfil cognitivo: la edad de inicio de la
epilepsia. Un inicio temprano se asocia con habilidades verbales inicialmente mayores,
una mayor discrepancia VIQ > PIQ, y un mayor declive posterior. Cabe anotarse que se
vio una gran variación entre los niños en términos de perfiles y perfiles de cambio, lo que
significa que si bien estos resultados describen el grupo, un caso individual puede mostrar
un patrón de cambio diferente.
Otras variables, tales como la participación en enseñanza regular o especial, y el
nivel socioeconómico (medido a partir del nivel educativo de los padres) se vieron
relacionados con el nivel cognitivo, pero no con los cambios a través del tiempo. Es decir,
los niños en educación especial tienen un nivel más bajo, pero no muestran mayor
deterioro; igualmente, los niños de nivel socioeconómico alto tienen un CI más alto, pero
el nivel socioeconómico no los “protege” del deterioro.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
158
El perfil VIQ > PIQ, aunque característico en niños con epilepsia, no se percibe en
todos los niños con epilepsia. En el Capítulo 6, la discrepancia entre la escala verbal y de
ejecución se estudió en dos muestras de niños con trastornos aislados y con
comorbilidades. La primera muestra incluyó 117 niños con trastornos “aislados”, es decir
niños con un solo diagnóstico (aunque podían tener otros problemas neuropsicológicos
que no condujeron a un segundo diagnóstico). Se incluyeron niños con epilepsia, con
trastornos de lectura, trastornos de matemáticas, y trastornos en el espectro autista. Se vio
que, en los niños con epilepsia, el perfil VIQ > PIQ (las habilidades verbales superaban
las de ejecución) era específico – los demás niños no mostraban este perfil. Los restantes
niños (y los del grupo control) mostraban un perfil plano o bien un perfil opuesto, VIQ <
PIQ, principalmente en trastornos de lectura. Evaluamos una segunda muestra de 171
niños, todos con epilepsia, pero algunos de ellos con doble diagnóstico de epilepsia y
comorbilidades (nuevamente: trastornos de lectura, de matemáticas, autismo).
Encontramos que los perfiles habían cambiado, y que eran parcialmente similares y
parcialmente diferentes a perfiles de los trastornos aislados. El impacto de este cambio era
similar en todos los trastornos. Donde se conjugan epilepsia y otro diagnóstico, se ve un
relativo deterioro de la escala de ejecución, mientras que la escala verbal se ve
relativamente resguardada.
En resumen, los estudios permiten urdir una descripción no conocida anteriormente sobre
el desarrollo del perfil cognitivo de niños con epilepsia a través del tiempo. Inicialmente,
el perfil VIQ > PIQ sugiere que las habilidades verbales se conservan y las de ejecución
se ven afectadas. Este perfil nos lleva a sugerir que la escala verbal es un indicador del
potencial original del niño con epilepsia, mientras que la escala de ejecución refleja la
vulnerabilidad del cerebro ante el trastorno epiléptico. Es posible que esta baja inicial de
la escala de ejecución sea un marcador cognitivo y sería interesante estudiar si ya se
percibe ante la inminencia de la epilepsia, anterior a su presentación. A través de los
primeros años, el nivel cognitivo desciende, más en los niños con epilepsia temprana que
en aquellos con epilepsia tardía, y ante todo en la escala verbal. A los dos años y medio,
un cuarto de los niños referidos ha sufrido un cambio significativo en la escala verbal.
Con el curso del tiempo, el deterioro se torna más lento, pero continúa a lo largo de los
años; es independiente de la persistencia de crisis, e incluye también cambios
significativos en la escala de ejecución. La presencia de comorbilidades influye en el
perfil en el sentido de que VIQ > PIQ cambia, modulado por el segundo trastorno. En
RESUMEN
159
epilepsia y trastornos de lectura, el perfil se torna plano; en epilepsia y trastornos de
matemáticas, el perfil VIQ > PIQ se hace más marcado aún, mientras en epilepsia y
autismo, no hay diferencias significativas entre los perfiles.
Al final del trabajo, los apéndices presentan “base rate tables”, que son tablas con los
porcentajes de niños que presentan diferencias de cierta magnitud en los WISCs. Las
tablas se basan en un total de más de mil niños neerlandeses, referidos por trastornos de
desarrollo (trastornos de aprendizaje, trastornos de conducta y psiquiátricos, y, ante todo,
epilepsia). También se incluyen datos de 88 niños control. Las tablas hacen relación a los
diferentes temas estudiados: (1) variabilidad entre las subpruebas o subtest scatter, (2)
discrepancias entre VIQ – PIQ y entre los factores y, (3) cambios de nivel cognitivo a lo
largo de varias evaluaciones neuropsicológicas, incluyendo cambios que se observan tras
el uso de una prueba de Wechsler diferente en la segunda evaluación, tal como el WISC-
RNL o WPPSI-IIINL seguido del WISC-IIINL. Las tablas incluyen datos de niños evaluados
posteriormente a la recolección de datos para los diferentes capítulos. En este sentido, las
tablas se relacionan a los capítulos pero a la vez son más extensas.
Appendices
APPENDICES
163
Appendices
It may be argued that summary statistics coming from the studies already include data on
individual children. Bringing these data to view, however, is considered of relevance for
the clinician. It is observed that Dutch clinicians often refrain from reporting IQs when
the discrepancy between the IQ scales is significant. Significant differences between the
verbal and the performance abilities, rather than being a reason for excluding the values
from the reports, may have importance for clinical and remediation purposes, and should
be understood rather than avoided. Similarly, subtest variability may be a relevant
descriptive of the child. It is also observed that IQs are excluded from reports when
subtest scatter appears increased while, in fact, in The Netherlands data providing the
clinician with psychometrically sound information on cut-offs or frequency of occurrence
are mostly lacking. Thus, the data of the Appendices aim at providing some of this
information.
Insights on the cognitive pattern and on magnitudes of cognitive change in
epilepsy may provide better understanding as to whether the pattern seen in a particular
child with epilepsy is common in children with epilepsy. Certainly, it should always be
borne in mind that an individual child may show a different pattern than the pattern
displayed frequently by other children. While the studies indicate the presence of distinct
cognitive patterns, of changing patterns over time and of changing patterns in
comorbidities, the studies also suggest that there is large variability between individuals
with epilepsy. This being said, the data analysed in the previous chapters allow for the
construction of tables, which may be of utility for the clinician.
In the Appendices A – D, the data are presented as base rate data. They relate to
the topics and participants discussed in the previous chapters. Efforts were done go
beyond these data and to report on larger samples than those discussed. Data collection on
epilepsy continued after the writing of the chapters, allowing for inclusion of more
participants than those presented in the chapters. For example, while the study on subtest
scaled-score range (Chapter 2) related to WISC-RNL only, in the base rate tables (Appendix
A), data on children with epilepsy and non-referred children tested with the WISC-IIINL are
included as well. Also, for the tables on VIQ – PIQ discrepancies (Appendix B), besides
the data from the participants discussed in Chapter 6, data from the participants of
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
164
Chapters 2 and 5, as well as newly collected data, were included as well. Data on
cognitive change at retesting were extended with data after a change of test version
(Appendix C). The vaster collection of WISC-IIINL data allowed for the construction of
tables on the factor index scores as well (Appendices C and D).
Appendix E provides the ROC curves which relate to the VIQ – PIQ and VCI –
POI data on children with isolated disorders versus comorbid disorders related in Chapter
6, as well as the rate of children showing significant discrepancies.
After testing a child with epilepsy, the clinician observed intra-individual subtest
variability and a VIQ – PIQ discrepancy of a particular magnitude. If case of a
reassessment, the clinician observed a difference between the first and the second test.
The clinician may want to know, how frequent these values were found in clinical or
standardization samples:
(1) Intra-individual subtest variability (subtest scatter) is the difference between
the highest and lowest subtest-scaled score in the verbal, performance and full
scales. How often is the observed scatter seen clinical comparison samples?
(Appendix A). Base rate data are given for the samples discussed in Chapter 2,
and additional WISC-IIINL data for children with epilepsy.
(2) The verbal – performance discrepancy is the directional VIQ – PIQ difference.
A positive value denotes VIQ > PIQ. A negative value denotes PIQ > VIQ.
How often does a directional difference between the verbal and performance
scale occur in clinical samples? In Appendix B, expected values are given, as
well as observed values from the samples discussed in Chapter 2, and
additional WISC-IIINL data on children with epilepsy. The discrepancy
between factor scores is establised pairwise (VCI – POI, VCI – PSI or POI –
PSI). How often does a specific difference between factor index scores occur?
In Appendix C, data are given on children with epilepsy tested with the WISC-
IIINL.
(3) A child has already been tested earlier (T1), and is tested for the second time
(T2), with an interval between the two tests of 12 months or more. For the
verbal, the performance and full scales (and factor index scores), T2 – T1 is
established. What percentage of children with epilepsy show this change of
APPENDICES
165
cognitive function? Data are provided for the verbal, performance and full
scales, as well as for the factor index scores (VCI, POI, PSI) in Appendix D.
Data are given for the children discussed in Chapter 4, additionaldata for a
larger sample tested with the WISC-IIINL, and small samples after changes in test
version (from WISC-RNL to WISC-IIINL; from WPPSI-IIINL to WISC-IIINL).
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
166
Appendix A
Subtest Scaled-Score Range (Subtest Scatter)
Subtest scaled-score range relates to the difference between the highest and the lowest
subtest score in a profile. For example, if the highest score on the verbal scale is 11 and
the lowest is 6, scatter is 5. The subtest scaled-score range of the principal data discussed
in Chapters 2 are presented in the following tables as base rate data. In addition to the
discussed samples, data on the WISC-IIINL in children with epilepsy are also provided.
Table A.1. gives the descriptives of the samples. Base rate data are displayed for the
verbal and performance scales in Table A.2 and for the full scale in Table A.3.
Expected means and (modified) standard deviations for subtest scaled score range were
established based on formula’s from Silverstein (1987) and tables from Owen (1962), and
applied as follows:
Meanscatter =
Where:
σ = 3 (SD of a WISC subtest)
ρ = mean intercorrelation of subtests (for 5 verbal subtests of the Dutch WISC-RNL = 0.55)
E(W) = value from Owen (1962, Table 6.2, p.140), for n = 5 (5 verbal subtests), = 2.326
SDscatter =
Where:
σ = 3 (SD of WISC subtest)
ρ = mean intercorrelation of subtests (for 5 verbal WISC-RNL = 0.55)
[σ2 E(W)] = a value from (1962, Table 6.2, p.140) for n = 5 (5 verbal subtests) = 0.747
The tables read as follows: on the verbal scale (A.2., upper panel), a scaled score range of
5 points or more was found on the WISC-IIINL in 54.5% of non-referred children and in
48.4% of the sample with epilepsy and close to normal IQ. On the full scale (A.3), scaled
score range of 8 points or more was found on the WISC-IIINL in 51.1% of non-referred
children and in 48.1% of the sample with epilepsy and close to normal IQ.
APP
END
IX A
Tabl
e A
.1. M
ixed
refe
rred
and
non
-ref
erre
d sa
mpl
es. C
hara
cter
istic
s of t
he sa
mpl
es.
Ex
pect
ed
N
ot re
ferr
ed
LD
Psyc
hiat
ry
Ep
ileps
y St
anda
rdiz
atio
n sa
mpl
es
Not
refe
rred
LD
Ps
ychi
atry
Ep
ileps
y Ep
ileps
y Ep
ileps
y Te
st W
ISC
-RN
L W
ISC
-III
NL
WIS
C-I
IIN
L W
ISC
-RN
L W
ISC
-RN
L W
ISC
-RN
L W
ISC
-III
W
ISC
-III
N
19
61
1239
88
13
2 17
8 15
7 22
1 63
Se
lect
ion
Stan
dard
izat
ion
sam
ples
75
< FS
-IQ
< 1
30
FS-I
Q >
75
FS-I
Q >
75
FS-I
Q >
75
FS-I
Q >
75
FS-I
Q ≤
75
Age
6:
0 to
16:
11
6:0
to 1
6:11
9.
0 (1
.8)
12.8
(1.3
) 10
.9 (2
.7)
9.7
(2.7
) 9.
9 (2
.9)
10.5
(2.7
) V
IQ
100.
0 (1
5.0)
10
0.0
(15.
0)
102.
5 (1
0.7)
93
.3 (1
0.8)
93
.8 (1
1.5)
95
.3 (1
2.1)
94
.4 (1
1.0)
67
.9 (1
2.4)
PI
Q
100.
0 (1
5.0)
10
0.0
(15.
0)
103.
6 (1
2.5)
97
.3 (1
2.3)
95
.7 (1
3.5)
91
.0 (1
1.9)
89
.3 (1
2.2)
64
.2 (1
1.6)
FS
IQ
100.
0 (1
5.0)
10
0.0
(15.
0)
103.
2 (1
0.6)
94
.6 (1
0.8)
93
.9 (1
0.7)
92
.5 (1
0.7)
91
.1 (1
0.7)
63
.6 (7
.6)
Ran
ge F
S-IQ
78
- 12
6 77
- 12
4 76
- 12
7 76
- 12
5 76
- 13
1 47
- 75
V
erba
l sca
tter
4.7
(1.8
) 4.
7 (1
.8)
4.8
(2.0
) 4.
8 (2
.0)
5.0
(2.0
) 5.
0 (2
.0)
4.6
(1.9
) 4.
3 (1
.7)
Perf
orm
ance
scat
ter
5.8
(2.1
) 5.
7 (2
.1)
6.3
(2.2
) 5.
8 (2
.3)
6.5
(2.6
) 6.
0 (2
.4)
5.4
(2.0
) 4.
5 (1
.7)
Full-
Scal
e sc
atte
r 7.
3 (1
.9)
7.4
(1.9
) 7.
6 (2
.1)
7.4
(2.1
) 8.
0 (2
.2)
7.7
(2.3
) 7.
0 (2
.1)
6.0
(2.2
) A
OE
5.6
(3.3
) 6.
0 (3
.3)
4.6
(3.5
) D
urat
ion
of e
pile
psy
4.0
(3.2
) 3.
9 (2
.9)
5.9
(3.7
)
Not
e. L
D =
spec
ific
lear
ning
dis
abili
ties.
PSY
: psy
chia
tric
diso
rder
s. EP
I = e
pile
psy.
AO
E =
age
at o
nset
of e
pile
psy.
Shad
ed =
sam
ples
dis
cuss
ed in
Cha
pter
167
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
168
Table A.2. Base rate tables referred and non-referred samples. Subtest Scaled-Score range
(subtest scatter) on 5 subtests of the verbal scale and on 5 subtests of the performance
scale.Cumulative percentages.
Verbal Scale Not referred LD Psychiatry Epilepsy
WISC-IIINL WISC-RNL WISC-RNL WISC-RNL WISC-III WISC-III Range FS-IQ > 75 FS-IQ > 75 FS-IQ > 75 FS-IQ > 75 FS-IQ ≤ 75 Range
13 13 12 12 11 2.3 0.8 0.6 0.5 11 10 3.4 2.3 1.1 2.5 1.4 10 9 5.3 6.2 6.4 3.2 1.6 9 8 8.0 8.3 12.9 15.3 8.1 6.3 8 7 20.5 17.4 25.3 21.0 16.3 9.5 7 6 27.3 31.1 36.0 25.0 28.1 17.5 6 5 54.5 53.0 51.1 54.1 48.4 42.9 5 4 72.7 74.2 77.5 75.2 68.8 66.7 4 3 93.2 90.9 90.4 91.1 85.1 90.5 3 2 97.7 97.0 87.8 98.7 98.6 95.2 2 1 100 99.2 100 100 100 100 1 0 100 0
Performance Scale Not referred LD Psychiatry Epilepsy
WISC-IIINL WISC-RNL WISC-RNL WISC-RNL WISC-III WISC-III Range FS-IQ > 75 FS-IQ > 75 FS-IQ > 75 FS-IQ > 75 FS-IQ ≤ 75 Range
16 16 15 0.6 15 14 0.8 0.6 14 13 1.5 2.8 1.3 13 12 1.1 3.4 3.8 12 11 5.7 2.3 6.2 5.7 0.9 11 10 8.0 7.6 12.9 7.0 2.3 10 9 14.8 11.4 23.0 13.4 5.0 9 8 30.7 24.2 30.9 20.4 14.9 4.8 8 7 43.2 32.6 46.1 37.6 28.1 11.1 7 6 60.2 49.2 63.5 52.2 48.4 30.2 6 5 76.1 69.7 77.0 70.1 66.5 49.2 5 4 89.8 87.1 8.2 89.2 82.4 68.3 4 3 97.0 97.0 96.1 96.8 94.1 87.3 3 2 100 99.2 98.9 99.4 98.6 98.4 2 1 100 100 100 100 100 1 0 0
APPENDIX A
169
Table A.3. Base rate tables on referred and non-referred samples. Subtest scaled-score range (subtest scatter) on 10 subtests of the Full scale.
Full Scale Not referred LD Psychiatry Epilepsy
WISC-IIINL WISC-RNL WISC-RNL WISC-RNL WISC-III WISC-III Range FS-IQ > 75 FS-IQ > 75 FS-IQ > 75 FS-IQ > 75 FS-IQ ≤ 75 Range
16 16 15 0.6 15 14 1.1 1.5 0.6 0.5 14 13 3.0 4.5 2.5 0.9 13 12 3.8 6.7 6.4 3.2 12 11 9.1 6.8 12.9 10.8 4.5 11 10 14.8 15.9 27.0 18.5 10.9 4.8 10 9 36.4 26.5 42.7 33.1 21.7 11.1 9 8 51.1 47.0 58.4 50.3 42.1 22.2 8 7 69.3 61.4 74.7 73.2 62.0 36.5 7 6 81.8 81.8 87.6 81.5 76.9 60.3 6 5 96.6 93.9 97.8 91.7 88.7 74.6 5 4 97.7 99.2 100 98.1 95.0 87.3 4 3 100 100 100 99.5 98.4 3 2 100 2 1 100 1 0 0
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
170
Appendix B
Base Rate Tables: Verbal – Performance Discrepancies (VIQ – PIQ)
Verbal – performance discrepancies relate to the VIQ – PIQ difference. If a child scores
VIQ 105 and PIQ 85, the 20-point difference favours the verbal scale. In appendix B,
base rate data for the verbal – performance discrepancies (VIQ – PIQ) are pesented. Table
B.1. gives the characteristics of the samples included. Table B.2. and B.3 display the base
rate data.The differences may favour the verbal scale (B.2.) or the performance scale
(B.3). The data presented are (a) expected rates for the WISC-RNL and the WISC-IIINL,
(b) observed data from the mixed samples of children with learning disabilitises,
psychiatric and behavioural disorders and epilepsy discussed in Chapter 2, and (c)
observed WISC-IIINL data for children with epilepsy (children included in Chapters 4 –
6).
Expected rates. Based on the overall correlation between the two scales, Sattler (1990,
p.819) provides the formula to calculate the expected proportions for different
magnitudes: , and therefore: ;
wherein discrepancy is the difference between VIQ and PIQ of interest, sd = SD is the
standard deviation of the test (SD =15 for the Wechsler tests), and r the correlation
between the two scales which is rV-P= .58 for the WISC-RNL (de Bruyn, Vandersteene, &
van Haasen, 1986, p144) and is rV-P= .56 for the WISC-IIINL (Wechsler, 2005, Table
3.12). For each z- value, the equivalent proportion from a standard normal distribution
was established. For the WISC-RNL, the thus calculated expected values and observed
values by Van Haasen et al. (1986, p 177) for the standardization group were found to be
virtually identical.
The tables read as follows: on the WISC-IIINL, a VIQ – PIQ discrepancy of 20 points or
more favouring the verbal scale is expected in 7.8% of the children of the standardization
sample, and is actually observed in 11.5% of children with epilepsy (Table B.2.). On the
WISC-IIINL, a VIQ – PIQ discrepancy of 20 points or more favouring the performance
scale is expected in 7.8% of the children of the standardization sample, and is actually
observed in 2.8% of children with epilepsy (Table B.3.).
APP
END
IX B
Tabl
e B
.1. C
hara
cter
istic
s of t
he sa
mpl
es u
sed
for T
able
s B.2
and
B.3
.
Sam
ple
Stan
dard
izat
ion
N
ot
refe
rred
LD
Ps
ychi
atric
Epile
psy
Test
W
ISC
-RN
L W
ISC
-IIIN
L W
ISC
-IIIN
L W
ISC
-RN
L W
ISC
-RN
L W
ISC
-RN
L W
ISC
-IIIN
L W
ISC
-IIIN
L N
19
61
1239
88
13
2 17
8 16
7 25
3 72
Se
lect
ion
stan
dard
izat
ion
FS-I
Q <
130
FS
-IQ
> 7
5 FS
-IQ
> 7
5 FS
-IQ
> 7
5 FS
-IQ
> 7
5 FS
-IQ
=<
75
Age
6:
0 to
16
:11
6:0
to
16:1
1 9.
0 (1
.8)
12.8
(1.3
) 10
.9 (2
.7)
9.5
(2.5
) 9.
7 (2
.8)
10.2
(2.6
)
VIQ
10
0.0
(15.
0)
100.
0 (1
5.0)
10
2.5
(10.
7)
93.3
(10.
8)
93.8
(11.
5)
95.8
(12.
3)
94.9
(11.
3)
69.5
(8.8
)
PIQ
10
0.0
(15.
0)
100.
0 (1
5.0)
10
3.6
(12.
5)
97.3
(12.
3)
95.7
(13.
5)
90.9
(12.
9)
90.3
(12.
8)
65.8
(8.7
)
FSIQ
10
0.0
(15.
0)
100.
0 (1
5.0)
10
3.2
(10.
6)
94.6
(10.
8)
93.9
(10.
7)
92.6
(11.
7)
92.0
(11.
2)
64.4
(7.5
) V
IQ-P
IQ
0.0
(15.
0)
0.0
(15.
0)
-1.1
(13.
6)
-4,0
5 (1
2.6)
-1
,88
(15.
7)
4.9
(13.
7)
4.4
(13.
2)
3.8
(11.
6)
Ran
ge F
S-IQ
78
- 12
6 77
- 12
4 76
- 12
7 76
- 12
5 76
- 13
1 47
- 75
A
ge a
t ons
et o
f epi
leps
y 5.
6 (3
.2)
5.9
(3.2
) 4.
6 (3
.4)
Dur
atio
n of
epi
leps
y
3.9
(3.3
) 3.
8 (2
.9)
5.7
(3.5
) N
ote.
LD
= m
ixed
sam
ple
lear
ning
dis
orde
rs. P
sych
iatri
c =
mix
ed sa
mpl
e ps
ychi
atric
dis
orde
rs.
171
C
OG
NIT
IVE
PATT
ERN
S IN
PA
EDIA
TRIC
EPI
LEPS
Y
Ta
ble
B.2
. Bas
e ra
te ta
ble
VIQ
> P
IQ.
Ver
bal -
Per
form
ance
disc
repa
ncy.
VIQ
- PI
Q f
avou
rs th
e V
erba
l Sca
le
Ex
pect
ed
N
ot re
ferr
ed
LD
Psyc
hiat
ry
Ep
ileps
y
VIQ
> P
IQ
WIS
C-R
NL
WIS
C-I
IIN
L W
ISC
-III
NL
WIS
C-R
NL
WIS
C-R
NL
WIS
C-R
NL
WIS
C-I
IIN
L V
IQ >
PIQ
FS
-IQ
> 7
5 FS
-IQ
> 7
5 FS
-IQ
> 7
5 FS
-IQ
> 7
5 FS
-IQ
=<
75
25
3.4
3.8
8.
4
4.2
25
24
4.0
4.4
9.6
5.9
24
23
4.7
5.2
5.7
4.5
12.0
6.
7 23
22
5.
5 5.
9 1.
5 15
.0
7.5
5.6
22
21
6.3
7.8
8.
0
2.3
5.
6
15.6
9.
5
21
20
7.4
7.8
3.
0
18.0
11
.5
6.9
20
19
8.4
8.9
10.2
6.
2 18
.6
13.0
8.
3 19
18
9.
5 10
.0
4.5
7.9
19.2
14
.6
11.1
18
17
10
.7
11.3
5.
3 9.
6 16
.6
12.5
17
16
12
.3
12.7
6.1
11
.8
23
.4
19.0
13
.9
16
15
13.8
14
.2
11.4
7.
6 14
.0
24.6
19
.8
20.8
15
14
15
.4
16.1
12
.5
9.1
15.7
26
.3
22.9
23
.6
14
13
17.1
17
.9
15.9
10
.6
16.3
28
.1
25.3
25
.0
13
12
19.2
19
.8
17.0
17
.4
29.9
27
.3
28.4
12
11
21
.2
21.8
18.2
20.8
32.9
29
.6
27.9
11
10
23
.3
23.9
21.6
12.9
23.0
37.7
33
.6
29.2
10
9
25.8
26
.1
14.4
24
.2
39.5
36
.4
33.3
9
8 28
.1
28.4
22
.7
17.4
27
.9
41.9
40
.3
36.1
8
7 30
.5
30.9
26
.1
18.2
30
.3
44.3
42
.7
41.7
7
6 33
.0
33.4
20.5
34.3
47.9
48
.6
45.8
6
5 35
.9
35.9
33.0
23.5
37.1
52.1
51
.8
48.6
5
4 38
.6
39.9
35
.2
25.0
40
.4
53.9
53
.4
50.0
4
3 41
.3
41.7
37
.5
27.3
44
.9
56.9
56
.1
54.2
3
2 44
.0
44.4
40
.9
31.8
45
.5
56.7
58
.9
2 1
47.2
47
.2
42.0
34
.8
47.2
60
.5
60.1
58
.3
1 0
50.0
50
.0
45
.5
37
.1
51
.1
63
.5
65.2
69
.4
0
172
APP
END
IX B
Ta
ble
B.3
. Bas
e ra
te ta
ble
VIQ
< P
IQ.
Ver
bal -
Per
form
ance
disc
repa
ncy.
VIQ
- PI
Q f
avou
rs th
e Pe
rfor
man
ce S
cale
Expe
cted
Not
refe
rred
LD
Ps
ychi
atry
Epile
psy
V
IQ -
PIQ
W
ISC
-RN
L W
ISC
-III
NL
WIS
C-I
IIN
L W
ISC
-RN
L W
ISC
-RN
L W
ISC
-RN
L W
ISC
-III
NL
VIQ
< P
IQ
FS-I
Q >
75
FS-I
Q >
75
FS-I
Q >
75
FS-I
Q >
75
FS-I
Q =
< 75
-25
3.4
3.8
9.1
8.4
-25
-24
4.0
4.4
9.8
9.0
1.8
1.6
-24
-23
4.7
5.2
10.6
10
.1
2.4
2.0
2.8
-23
-22
5.5
5.9
4.5
11.8
4.
2 -2
2 -2
1 6.
3 7.
8 5.
7 11
.4
13.5
2.
4 -2
1 -2
0 7.
4 7.
8
15.2
2.
8
-20
-19
8.4
8.9
12.9
4.
8 3.
2 -1
9 -1
8 9.
5 10
.0
8.0
13.6
5.
4 3.
6 -1
8 -1
7 10
.7
11.3
15
.9
19.1
6.
6 -1
7 -1
6 12
.3
12.7
12
.5
17.4
21
.3
4.7
4.2
-16
-15
13.8
14
.2
14
.8
22
.5
7.
8 6.
3
-15
-14
15.4
16
.1
17.0
21
.2
24.2
9.
0 6.
7 8.
3 -1
4 -1
3 17
.1
17.9
20
.5
25.3
9.
6 8.
7 -1
3 -1
2 19
.2
19.8
23
.9
22.0
26
.4
10.2
9.
7 -1
2 -1
1 21
.2
21.8
28.4
25.8
28.7
13.8
10
.3
11.1
-1
1 -1
0 23
.3
23.9
33.0
31.1
30.3
15.0
11
.1
12.5
-1
0 -9
25
.8
26.1
36
.4
32.6
33
.1
16.2
13
.0
13.9
-9
-8
28
.1
28.4
7.
5 34
.8
36.0
17
.4
15.0
16
.7
-8
-7
30.5
30
.9
38.6
37
.9
37.6
18
.6
17.8
19
.4
-7
-6
33.0
33
.4
40
.9
40
.9
39
.3
21
.0
21.3
22
.2
-6
-5
35.9
35
.9
44.3
45
.5
22.8
24
.1
23.6
-5
-4
38
.6
39.9
47
.7
49.2
42
.1
26.3
27
.3
25.0
-4
-3
41
.3
41.7
48
.9
53.8
46
.1
29.9
28
.9
-3
-2
44.0
44
.4
54.5
57
.6
46.6
34
.1
33.2
27
.8
-2
-1
47.2
47
.2
62.9
48
.9
36.5
34
.8
30.6
-1
0
50.0
50
.0
58
.0
65
.2
52
.8
39
.5
39.9
41
.7
0
173
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
174
Appendix C
Base Rate Tables for the Discrepancies Between Factor Index Scores Verbal
Comprehension (VCI), Perceptual Organization (POI) and Processing Speed (PSI):
(VCI – POI , VCI – PSI, POI – PSI)
Chapter 6 analyzed the VCI – POI discrepancies alongside with the VIQ – PIQ
discrepancies, and discussed the POI – PSI briefly as well. The Discussion highlighted
that, throughout the decades, the WISC series have changed from relying mostly on the
scales (VIQ and PIQ) to relying increasingly on the factor scores (VCI, POI, PSI). In the
following tables, empirical base rate data for non-referred, typically developing control
children and for children with epilepsy tested with the WISC-IIINL are provided for pairwise
comparisons of the factor index scores. The data are based on the participants discussed
in Chapters 4 to 6, as well as data collected later on. No expected values are included,
given that the Dutch WISC-IIINL test manual does not provide the correlations between the
factor scores.
Table C.1. gives the descriptives of the samples. Tables C.2. displays the base rate
data for VIQ – POI, Table C.3. for VIQ – PSI and Table C.4. for PSI – PSI.
For example, a child (FS-IQ 93) gained scores on the WISC-IIINL factors as follows:
VIQ 100, POI 90, PSI 80.The tables read: A VCI > POI difference of 10 points or more is
observed in 19.3% of the non-referred children and in 31.7% of the children with epilepsy
(Table C.2., left panel). A VCI > PSI difference of 20 or more points is observed in
14.8% and 16.7% of the non-referred children and children with epilepsy, respectively
(Table C.3., left panel). A POI > PSI difference of 10 or more points is observed in 22.7%
and 28.0% of the non-referred children and children with epilepsy, respectively (Table
C.4., right panel).
APPENDIX C
175
Table C.1. Characteristics of the samples used for Tables C.2 and C.3.
Sample Not referred Epilepsy
Test WISC-IIINL WISC-IIINL WISC-IIINL N 88 246 66 Selection FS-IQ < 130 FS-IQ > 75 FS-IQ =< 75 Age 9.1 (1.9) 10.1 (3.1) 10.4 (2.9) VCI 102.3 (11.5) 95.4 (11.2) 71.0 (9.2) POI 103.3 (12.3) 91.2 (12.8) 67.0 (10.0) PSI 104.7 (14.6) 91.6 (14.2) 72.8 (12.2) VCI - POI -1.0 (13.2) 4.2 (13.8 4.0 (12.9) VCI - PSI -2.4 (18.3) 3.8 (17.4) -1.6 (13.2) POI - PSI -1.4 (18.0) -0.3 (16.3) -5.6 (13.2) AOE - 6.1 (3.4) 4.5 (3.4) Duration of epilepsy - 4.0 (3.2) 5.9 (3.8)
Note. VCI = Verbal Comprehension Index. POI = Perceptual Organization Index, PSI = Processing Speed Index. VCI – POI: mean difference between VCI and POI. AOE = Age at onset of epilepsy.
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
176
Table C.2. Base rate tables VCI > POI and VCI < POI.
VCI - POI favours Verbal Comprehension VCI - POI favours Perceptual Organization Non referred Epilepsy Non referred Epilepsy WISC-IIINL WISC-IIINL WISC-IIINL WISC-IIINL VCI - POI 75 <FS-IQ > 130 FS-IQ >75 FS-IQ <= 75 VCI - POI 75 <FS-IQ > 130 FS-IQ > 75 FS-IQ <= 75
> 53 0.4 -54 0.4 Ζ Ζ 40 -40 0.8 39 1.1 1.2 -39 38 -38 37 2.0 -37 36 2.4 -36 35 -35 34 2.3 2.8 1.5 -34 33 3.3 -33 32 -32 31 3.4 -31 30 3.7 -30 1.2 29 4.1 -29 28 4.5 -28 27 4.9 -27 1.1 1.6 26 5.7 -26 25 3.0 -25 2.0 3.0 24 6.5 4.5 -24 23 .5 7.3 -23 2.4 22 5.7 8.5 6.1 -22 4.5 4.5 21 6.8 9.8 9.1 -21 6.1 20 8.0 10.6 13.6 -20 19 9.1 13.4 15.2 -19 6.8 3.3 7.6 18 12.5 15.9 16.7 -18 9.1 4.1 9.1 17 17.1 21.2 -17 11.4 5.3 16 19.1 22.7 -16 15 21.5 24.2 -15 13.6 6.9 10.6 14 13.6 22.8 -14 15.9 9.8 13 23.2 -13 18.2 10.2 12 15.9 24.8 -12 19.3 12.1 11 17.0 28.0 -11 20.5 10.6 15.2 10 19.3 31.7 27.3 -10 27.3 12.6 16.7 9 20.5 35.8 34.8 -9 29.5 13.4 18.2 8 22.7 38.6 40.9 -8 33.0 15.4 7 23.9 40.7 43.9 -7 39.8 17.5 19.7 6 28.4 43.1 45.5 -6 43.2 19.5 5 31.8 45.1 48.5 -5 44.3 23.2 4 33.0 49.6 53.0 -4 46.6 26.8 21.2 3 54.1 59.1 -3 51.1 30.1 22.7 2 36.4 55.7 62.1 -2 55.7 32.1 28.8 1 37.5 59.8 68.2 -1 36.2 30.3 0 44.3 63.8 69.7
APPENDIX C
177
Table C.3. Base rate tables VCI > PSI and VCI < PSI.
VCI -PSI favours Verbal Comprehension VCI -PSI favours Processing Speed Non referred Epilepsy Non referred Epilepsy WISC-IIINL WISC-IIINL WISC-IIINL WISC-IIINL VCI - PSI 75 <FS-IQ > 130 FS-IQ > 75 FS-IQ <= 75 VCI - PSI 75 <FS-IQ > 130 FS-IQ > 75 FS-IQ <= 75
>=53 0.8 -54 Ζ ζ 40 3.3 -40 39 -39 3.4 1.6 38 -38 4.5 37 -37 36 1.1 3.7 -36 2.0 35 4.5 -35 2.8 34 2.3 4.9 -34 3.0 33 4.5 -33 5.7 3.3 32 5.3 -32 31 5.7 -31 3.7 30 6.1 -30 9.1 29 6.9 -29 4.5 28 6.8 7.7 -28 27 8.0 8.1 -27 10.2 4.9 26 9.1 8.9 1.5 -26 4.5 25 9.3 -25 6.1 24 10.2 10.2 -24 5.7 23 11.4 13.4 3.0 -23 11.4 9.1 22 14.6 4.5 -22 12.5 6.1 21 13.6 15.4 -21 20 14.8 16.7 6.1 -20 7.3 19 17.1 -19 14.8 8.9 10.6 18 17.9 -18 15.9 9.3 12.1 17 18.7 7.6 -17 23.9 11.4 13.6 16 17.0 22.0 9.1 -16 26.1 13.4 15 19.3 24.8 -15 28.4 13.8 16.7 14 26.0 -14 30.7 14.6 18.2 13 23.9 28.9 12.1 -13 15.9 12 32.5 -12 17.1 11 34.6 15.2 -11 31.8 19.5 10 25.0 37.8 -10 35.2 21.1 9 26.1 41.1 21.2 -9 23.2 21.2 8 42.7 24.2 -8 37.5 25.6 25.8 7 28.4 43.9 25.8 -7 39.8 26.8 28.8 6 31.8 46.7 -6 40.9 27.6 31.8 5 33.0 49.6 27.3 -5 47.7 30.9 34.8 4 26.4 52.0 28.8 -4 52.3 33.3 37.9 3 37.5 54.5 40.9 -3 54.5 33.7 42.4 2 56.1 43.9 -2 58.0 36.6 48.5 1 38.6 58.9 47.0 -1 59.1 38.6 50.0 0 40.9 61.4 50.0
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
178
Table C.4. Base rate tables POI > PSI and POI > PSI.
POI - PSI favours Perceptual Organization POI - PSI favours Processing Speed Non referred Epilepsy Non referred Epilepsy WISC-IIINL WISC-IIINL WISC-IIINL WISC-IIINL POI - PSI 75 <FS-IQ > 130 FS-IQ > 75 FS-IQ <= 75 POI - PSI 75 <FS-IQ > 130 FS-IQ > 75 FS-IQ <= 75
47 1.1 0.4 -48 1.1 Ζ ζ 40 -40 1.2 1.5 39 -39 1.6 38 1.2 -38 37 -37 2.3 36 4.5 -36 35 -35 34 5.7 2.0 -34 33 6.8 -33 32 -32 3.4 31 2.4 -31 2.4 30 -30 29 2.8 -29 3.3 28 8.0 -28 4.5 4.1 27 4.5 -27 5.7 4.5 3.0 26 9.1 4.9 -26 6.8 6.1 25 5.7 1.5 -25 8.0 7.6 24 6.1 -24 9.1 6.9 9.1 23 8.9 -23 10.2 8.5 10.6 22 9.3 3.0 -22 9.3 12.1 21 10.2 4.5 -21 14.8 11.4 20 10.2 11.4 -20 15.9 13.8 19 11.4 13.0 -19 17.0 14,2 16.7 18 12.5 14.6 -18 15.4 19.7 17 13.6 15.4 -17 18.2 18.3 22.7 16 14.8 18.3 -16 19.3 19.9 28.8 15 15.9 19.1 -15 30.3 14 20.7 -14 21.6 22.4 31.8 13 18.2 21.5 -13 25.0 23.6 36.4 12 22.0 -12 26.1 26.0 37.9 11 20.5 25.6 10.6 -11 29.5 28.0 39.4 10 22.7 28.0 13.6 -10 31.8 28.9 9 23.9 30.1 16.7 -9 37.5 30.9 42.4 8 27.3 32.9 19.7 -8 38.6 31.7 43.9 7 29.5 34.6 -7 33.7 47.0 6 31.8 38.2 21.2 -6 45.5 39.0 50.0 5 41.1 22.7 -5 46.6 40.2 4 37.5 41.5 -4 47.7 42.3 54.5 3 43.5 27.3 -3 54.5 45.9 2 40.9 44.7 30.3 -2 47.6 57.6 1 43.2 46.7 33.3 -1 56.8 49.2 60.6 0 40.9 50.8 39.4
APPENDIX D
179
Appendix D
Base Rate Tables: From Test 1 to Test 2 - Cognitive Change After Serial Testing
Chapter 4 studied reliable cognitive change in children with epilepsy tested twice with the
same test version, either the WISC-RNL or the WISC-IIINL. In the following tables, the results
of cognitive change after serial testing will be presented. The data relate to children with
epilepsy tested twice with the Wechsler scales. The children may have been tested twice
with the same test version (T1 = T2, as in Chapter 4) or a change of test version may have
occurred (T1 ≠ T2, from WISC-RNL to WISC-IIINL or from WPPSI-IIINL to WISC-IIINL). Cut-offs
were calculated based on empirical data for referred children with developmental
disorders but without epilepsy (Schittekatte, 2005). No data on typically developing
children are provided. It should be noted that samples are relatively small, which means
that the results should be interpreted with caution.
Table D.1. gives the descriptives of the samples. Base rates data on cognitive
gains and losses are presented for the verbal scale in Table D.2, for the performance scale
in Table D.3., for the full scale in Table D.4. Table D.5 provides the descriptives for the
sample used for the factor index scores (VCI, POI, PSI). Base rate data in cognitive gains
and losses are presented in Table D.6 for the factor index scores.
For example, a child with epilepsy may have shown a VIQ 99 at first testing (T1)
with the WISC-IIINL, and a VIQ 85 at second testing, also with the WISC-IIINL. The tables
read as follows: After testing a child with epilepsy twice with the WISC-IIINL, a loss of 14
or more on the verbal scale would be expected in 5% of referred children (Table D.1.),
but was actually seen in 22.5% of the children (last column D.2.)
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
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Table D.1. Characteristics of the samples used for Tables D.2. to D.4.
Epilepsy
Test T1 WISC-RNL/IIINL WPPSI-IIINL WISC-RNL WISC-IIINL Test T2 WISC-RNL/IIINL WISC-IIINL WISC-IIINL WISC-IIINL N 41 + 32 = 73 30 26 80 Test version T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2
T1 Age at onset of epilepsy 5.4 (3.0) 2.6 (1.8) 5.0 (1.9) 5.5 (2.8) Age 9.1 (2.2) 5.7 (0.6) 8.9 (2.0) 8.5 (2.1) Duration epilepsy at T1 3.7 (3.0) 3.1 (1.8) 3.9 (2.5) 3.0 (2.5) VIQ 90.1 (15.5) 85.8 (13.0) 88.4 (13.7) 91.2 (12.0) PIQ 87.5 (16.3) 81.4 (15.5) 80.7 ( 16.0) 86.1 (14.5) FSIQ 88.1 (15.9) 81.4 (13.7) 82.9 (13.7) 87.7 (13.2) VIQ-PIQ 3.4 (13.5) 4.4 (14.9) 7.7 (15.1) 5.2 (11.7)
T2 Age 11.4 (2.3) 8.7 (1.7) 13.3 (2.8) 11.1 (2.2) Duration epilepsy at T2 6.0 (3.1) 6.1 (2.7) 8.4 (3.4) 5.6 (2.7) VIQ 83.7 (16.3) 80.9 (15.8) 79.0 (14.5) 84.4 (12.7) PIQ 86.9 (17.8) 75.9 (13.4) 74.8 (14.5) 83.9 (15.4) FSIQ 83.7 (17.0) 76.3 (13.6) 74.2 (14.6) 82.4 (14.1) VIQ-PIQ -3.2 (13.7) 5.0 (15.4) 4.3 (12.7) 0.5 (10.7)
ΔT2-T1 Interval T1 T2 2.3 (1.2) 3.0 (1.7) 4.4 (2.2) 2.6 (1.3) VIQ -7.2 (11.3) -4.9 (12.9) -9.4 (8.2) -6.9 (9.2) PIQ -0.6 (11.9) -5.5 (12.8) -6.0 (13.4) -2.1 (9.8) FSIQ -4.4 (11.1) -5.1 (11.7) -8.7 (9.8) -5.3 (9.3) VIQ-PIQ -6.6 (11.4) 0.6 (12.6) -3.4 (12.6) -4.7 (9.0)
cut-off RCI gain / loss VIQ IQ points 14 / 14 N.A. 10 / 19 14 / 14 PIQ IQ points 18 / 18 N.A. 18 / 18 18 / 18 FS-IQ IQ points 14 / 14 N.A. 11 / 17 14 / 14
Note. T1 = test 1, T2 = test 2. ΔT2-T1 = difference T2 minus T1 . Shaded = discussed in Chapter 4. RCI = Reliable cognitive change cut-off scores for the 90% confidence interval. If the same test version (WISC-RNL or WISC-IIINL) is given at T1 and T2, on the verbal scale a 14 or more point gain or 14 or more point loss is expected in 5% of the children. If a change of test version has occurred (WISC-RNL at T1 and WISC-IIINL at T2), cut-off scores are adjusted. On the verbal scale, in 5% of the children a gain is expected of 10 or more points, in 5% a loss of 19 or more points. For the rationale and formula’s, see Chapter 4. Minimum time interval between T1 and T2 is 12 months. Age, age at onset, duration of epilepsy and interval are expressed in years.
APPENDIX D
181
Table D.2. Cognitive gains and cognitive losses on the verbal scale.
Cognitive gains Verbal Scale Cognitive losses Verbal Scale
Epilepsy Epilepsy
T1 WISC-
RNL/IIINL WPPSI-
IIINL WISC-
RNL WISC-IIINL T1
WISC-RNL/IIINL
WPPSI-IIINL
WISC- RNL
WISC-IIINL
T2 WISC-
RNL/IIINL WISC-IIINL WISC-IIINL
WISC-IIINL T2
WISC-RNL/IIINL WISC-IIINL
WISC-IIINL
WISC-IIINL
Δ T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2 Δ T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2 ≥30 ≤30 5.5 2.5 29 -29 3.8 28 -28 7.7 27 3.3 -27 26 -26 6.8 3.8 25 -25 5.0 24 -24 23 -23 6.7 7.5 22 -22 11.5 8.8 21 1.4 -21 8.2 13.3 20 -20 9.6 19 -19 11.0 11.3 18 -18 12.3 16.7 17 -17 13.7 20.0 12.5 16 -16 16.4 23.3 15 1.3 -15 20.5 15.4 17.5 14 6.7 -14 26.0 22.5 13 10.0 -13 28.8 31.3 12 2.7 13.3 -12 37.0 30.0 33.8 11 -11 34.6 10 4.1 -10 38.4 50.0 36.3 9 2.5 -9 41.1 33.3 53.8 37.5 8 23.3 -8 45.2 40.0 57.7 40.0 7 6.8 3.8 -7 53.4 50.0 61.5 53.8 6 8.2 5.0 -6 54.8 60.0 55.0 5 11.0 7.5 -5 65.4 57.5 4 13.7 10.0 -4 69.2 3 15.1 15.0 -3 61.6 84.6 63.7 2 21.9 7.7 22.5 -2 65.8 63.3 70.0 1 23.3 30.0 26.3 -1 69.9 66.7 92.3 72.5 0 76.7 70.0 73.8
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
182
Table D.3. Cognitive gains and cognitive losses on the performance scale.
Cognitive gains Performance Scale Cognitive losses Perfromance Scale Epilepsy Epilepsy
T1 WISC-
RNL/IIINL WPPSI-
IIINL WISC-
RNL WISC-IIINL T1
WISC-RNL/IIINL
WPPSI-IIINL
WISC- RNL
WISC-IIINL
T2 WISC-
RNL/IIINL WISC-IIINL WISC-IIINL
WISC-IIINL T2
WISC-RNL/IIINL WISC-IIINL
WISC-IIINL
WISC-IIINL
Δ T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2 Δ T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2
≥30 ≤30 29 1.4 -29 6.7 28 -28 7.7 27 -27 10.0 11.5 26 1.6 -26 25 -25 24 2.7 -24 4.1 15.4 1.3 23 2.5 -23 5.5 22 4.1 -22 2.5 21 -21 3.8 20 3.3 -20 19.2 19 5.5 3.8 -19 18 -18 13.3 17 6.8 -17 8.2 20.0 5.0 16 8.2 3.8 -16 9.6 6.3 15 -15 11.0 23.3 7.5 14 9.6 7.7 5.0 -14 12.3 26.7 30.8 11.3 13 11.0 -13 15.1 34.6 13.8 12 -12 17.5 11 13.7 11.5 7.5 -11 19.2 30.0 42.3 23.8 10 15.1 6,7 10.0 -10 20.5 46.2 9 16.4 10.0 17.5 -9 23.3 33.3 31.3 8 13.3 15.4 -8 50.0 32.5 7 20.5 16.7 23.1 18.8 -7 26.0 36.3 6 21.9 20.0 20.0 -6 27.4 46.7 53.8 37.5 5 26.0 26.9 26.3 -5 28.8 40.0 4 32.9 23.3 30.8 28.7 -4 31.5 53.3 57.7 43.8 3 37.0 26.7 34.6 30.0 -3 3.2 56.7 61.5 48.8 2 42.5 30.0 38.5 35.0 -2 60.0 52.5 1 53.4 33.3 37.5 -1 35.6 66.7 55.0 0 46.6 62.5
APPENDIX D
183
Table D.4. Cognitive gains and cognitive losses on the full scale.
Cognitive gains Full Scale Cognitive losses Full Scale Epilepsy Epilepsy
T1 WISC-
RNL/IIINL WPPSI-
IIINL WISC-
RNL WISC-IIINL T1
WISC-RNL/IIINL
WPPSI-IIINL
WISC- RNL
WISC-IIINL
T2 WISC-
RNL/IIINL WISC-IIINL WISC-IIINL
WISC-IIINL T2
WISC-RNL/IIINL WISC-IIINL
WISC-IIINL
WISC-IIINL
Δ T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2 Δ T1 = T2 T1 ≠ T2 T1 ≠ T2 T1 = T2
≥30 ≤30 4.1 1.3 29 -29 6.7 3.8 28 -28 27 -27 26 -26 7.7 25 -25 24 -24 11.5 23 -23 2.5 22 -22 3.8 21 1.3 -21 10.0 15.4 20 -20 13.3 5.0 19 -19 6.8 16.7 6.3 18 -18 8.2 20.0 23.1 8.8 17 1.4 -17 9.6 23.3 10.0 16 3.3 -16 12.3 26.9 12.5 15 -15 13.7 15.0 14 2.7 6.7 -14 16.4 17.5 13 4.1 2.5 -13 23.3 26.3 12 -12 26.0 26.7 30.8 28.7 11 5.5 5.0 -11 30.0 34.6 30.0 10 6.8 10.0 3.8 6.3 -10 27.4 42.3 31.3 9 8.2 13.3 -9 32.9 33.3 46.2 40.0 8 12.3 -8 35.6 57.7 42.5 7 16.7 7.5 -7 39.7 36.7 48.8 6 16.4 11.3 -6 41.1 43.3 52.5 5 20.5 23.3 7.7 17.5 -5 43.8 50.0 61.5 58.8 4 24.7 21.3 -4 46.8 53.3 3 28.8 22.5 -3 49.3 76.9 61.3 2 31.5 26.7 15.4 25.0 -2 54.8 63.3 62.5 1 32.9 36.7 28.7 -1 61.6 65.0 0 67.1 71.3
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
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Table D.5. Cognitive Change on the Factor Index scores of the WISC-IIINL. Characteristics of the samples used for Table D.6.
Epilepsy
Test T1 WISC-IIINL
Test T2 WISC-IIINL N 60 Test version T1 = T2
T1 Age at onset of epilepsy 6.2 (2.9) Age 8.7 (2.2) Duration epilepsy at T1 2.5 (2.2) VCI 94.3 (12.3) POI 90.3 (15.1) PSI 87.3 (16.1)
T2 Age 11.4 (2.2) Duration epilepsy at T2 5.2 (2.3) VCI 87.7 (12.2) POI 86.7 (16.1) PSI 86.5 (14.7)
ΔT2-T1 Interval T1 T2 2.7 (1.2) VCI 6.5 (9.8) POI 3.6 (9.7) PSI 0.8 (12.5)
cut-off RCI gain / loss VCI IQ pointsa 14 / 14 POI IQ pointsa 18 / 18
PSI IQ pointsb 15 / 15 Note. Based on children tested twice with the WISC-IIINL for whom all factor index scores were available. a = estimated cut-off scores. The empirical values for the WISC-IIIUS (Canivez & Watkins, 1998), converted into cut-off scores (as in Chapter 4), lead to equal values for the verbal scale and verbal factor (VIQ and VCI) as well as for the performance scale and perceptual factor (PIQ and POI). Therefore, for the Dutch situation, cut-offs for VCI and POI are set equal to those calculated for VIQ and PIQ, respectively. b = estimated cut-off score for PSI. Given that the tasks for PSI are identical for the Dutch and American WISC-III, the cut-off estimated for the WISC-IIIUS is applied to the WISC-IIINL
APPENDIX D
185
Table D.6. Base Rate Table on Cognitive Gains (T1 > T2) (left) and Losses (right) on the
Factor Index scores Verbal Comprehension (VCI), Perceptual Organization (POI) and
Processing Speed (PSI)
Cognitive gains on Factor Indexes Cognitive loss on Factor Indexes Epilepsy Epilepsy
T1 WISC-IIINL T1 WISC-IIINL T2 WISC-IIINL T2 WISC-IIINL Δ T1 = T2 Δ T1 = T2 VCI POI PSI VCI POI PSI
42 1.7 -37 1.7 ζ ζ -33 1.7 ζ
30 3.3 -30 3.3 29 -29 28 -28 5.0 27 -27 6.7 26 -26 25 -25 24 1.7 -24 3.3 23 -23 1.7 22 -22 5.0 5.0 21 -21 20 3.3 -20 10.0 8.3 19 -19 18 5.0 -18 10.0 17 -17 6.7 11.7 16 1.7 6.7 -16 13.3 8.3 13.3 15 -15 18.3 10.0 15.0 14 10.0 -14 21.7 18.3 18.3 13 -13 23.3 12 5.0 -12 20.0 20.0 11 6.7 11.7 -11 25.0 25.0 10 5.0 8.3 -10 30.0 21.7 9 10.0 -9 33.3 30.0 23.3 8 13.3 16.7 -8 41.7 33.3 7 20.0 -7 45.0 46.7 25.0 6 8.3 15.0 26.7 -6 53.3 28.3 5 10.0 31.7 -5 55.0 51.7 33.3 4 13.3 21.7 -4 58.3 3 16.7 25.0 38.3 -3 61.7 55.0 38.3 2 28.3 41.7 -2 68.3 40.0 1 43.3 -1 71.7 56.7 0 83.3 71.7 56.7
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
186
Appendix E
Isolated Epilepsy, Isolated Developmental Disorders and Comorbidities in Epilepsy:
ROC Images for Chapter 6
The verbal – non-verbal discrepancies (VIQ – PIQ and VCI – POI) of the data in Chapter
6 were further analysed with Receiver Operating Characteristics (ROC).
ROC is a measure of diagnostic accuracy used to differentiate two samples from
each other. ROC uses all possible cut-off scores of the discrepancy (VIQ – PIQ or VCI –
POI) to compare two samples. Specificity (true positive scores, for example the group
with isolated epilepsy showing a specific VIQ – PIQ discrepancy) is contrasted to 1-
specificity (“false positive scores”, the control group showing this discrepancy). The Area
under the Curve (AUC) is calculated. AUC can take a value between .0 and 1.0, where
random scores appear close to .5. Scores between .5 and .7 denote a low discriminatory
accuracy, values between .7 and .9 denote moderate accuracy, and values above .9 denote
a high accuracy (Watkins, Glutting, & Youngstrom, 2005). A value of, for example, .640
for the contrast between non-referred controls and isolated epilepsy, can be read as
follows (Devena & Watkins, 2012): if a non-referred child is randomly selected from a
sample of non-referred children and a child with epilepsy is randomly selected from a
sample with epilepsy, the child with epilepsy would have a higher VIQ – PIQ (VIQ >
PIQ) difference about 64% of the time.
Isolated epilepsy was contrasted to control children (Figure E.1.) and isolated
epilepsy from Sample 1 was contrasted to isolated epilepsy from Sample 2 (Figure E.2.).
The isolated conditions were contrasted to the conditions comorbid with epilepsy:
isolated reading disorders versus comorbid reading disorders (Figure E.3), isolated math
disorders versus comorbid math disorders (Figure E.4), and isolated autism spectrum
disorders (ASD) versus comorbid ASD (Figure E.5.).
In addition, percentages of children showing “significant” directional VIQ – PIQ
discrepancies of 15 or more IQ points are presented in Table E. Based on the correlation
between the scales and the formula given by Sattler (1990, p.819); and a SDWechsler IQ =
15, the expected rate for a cut-off of 15 points was calculated: 13.8% for the WISC-RNL and
14.2% for the WISC-IIINL. The values are tested against the expected value (13.8% was
applied to all) with chi-square test.
APPENDIX E
187
Figure E.1.
Isolated epilepsy versus non-referred control
children.
Non-referred control children n = 82, isolated
epilepsy n = 139. VIQ – PIQ: AUC = .640, SE =
0.039, p = .001, [95%CI: .56, .72]. VCI – POI:
AUC = .637, SE = 0.042, p = .002, [95%CI: .55,
.72]
Figure E.2.
Isolated epilepsy.Two different samples.
Isolated epilepsy Sample 1, n = 39, WISC-RNL;
isolated epilepsy Sample 2, n = 100, WISC-IIINL.
VIQ – PIQ: AUC = .479, SE = 0.056, p = .708,
[95%CI: .37, .59]. VCI – POI: AUC = .483, SE =
0.057, p = .761, [95%CI: .37, .59]. Note equality
between the two samples.
Figure E.3.
Reading disorders. Isolated reading disorders
versus reading disorders comorbid with
epilepsy.
Sample 1, isolated reading disorders, n = 29;
Sample 2, reading disorders comorbid with
epilepsy, n = 31.VIQ – PIQ: AUC = .680, SE =
0.072, p = .017, [95%CI: .54, .82]. VCI – POI:
AUC = .651, SE = 0.072, p = .044, [95%CI: .51,
.79]
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
188
Figure E.4.
Math disorders. Isolated math disorders
versus math disorders comorbid with epilepsy.
Sample 1: Isolated math disorders, n = 25;
Sample 2, comorbid math disorders, n = 19. VIQ
– PIQ: AUC = .734, SE = 0.076, p = .009,
[95%CI: .58, .88]. VCI – POI: AUC = .733, SE =
0.085, p = .009, [95%CI: .59, .89]. Note the
moderate discriminatory value.
Figure E.5.
ASD: Isolated autism spectrum disorders
versus autism spectrum disorders comorbid
with epilepsy.
Sample 1: Isolated ASD, n = 24; Sample 2,
comorbid ASD n = 21. VIQ – PIQ: AUC = .589,
SE = 0.086, p = .306, [95%CI: .42, .76]. VCI –
POI: AUC = .531, SE = 0.088, p = .724, [95%CI:
.36, .70].
Table E. Rate of children showing VIQ – PIQ discrepancies of 15 or more points.
VIQ < PIQ VIQ > PIQ Sample % χ2 p % χ2 p Non-referred control 13.6 0.003 .954 12.3 .144 .704 Isolated epilepsy 5.0 8.975 .003 20.9 5.830 .016 Reading disorders (isolated) 27.6 4.633 .031 6.9 1.162 .281 Reading disorders (comorbid) 9.7 1.133 .287 6.4 2.385 .125 Math disorders (isolated) 8.0 0.707 .400 12.0 0.068 .794 Math disorders (comorbid) 0.0 - - 36.8 8.480 .004 ASD (isolated) 29.2 4.764 .029 20.8 0.998 .318 ASD comorbid) 4.8 1.442 .230 19.0 0.486 .486 Note. Chi-square testing against the expected value of 13.8%. Comorbid conditions = comorbid with epilepsy.
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Publications and Posters (related to the topics)
van Iterson, L. (2006). Different rates of directional VIQ – PIQ discrepancies in special education samples: below average IQ, learning disabilities, childhood psychiatry, and epilepsy. Poster presented at the INS/ SVNP / GNP conference: From plasticity to rehabilitation, Zürich, July 26 - 29.
van Iterson, L. (2007). Kaufman factors in three groups of special school children: learning disabilities. childhood psychiatry, and epilepsy. Poster presented at the International Neuropsychological Society, Federation of Spanish Societies of Neuropsychology, Spanish Neuropsychological Society, Spanish Psychiatry Society. Joint Mid-year meeting., Bilbao, July 4 - 7.
van Iterson, L. (2010). Failing to progress in secondary school and Reliable Changes in cognitive functioning in adolescents with epilepsy (Falta de progreso escolar en ensenanza secundaria y Cambios Confiables (RC) de nivel cognitivo en adolescentes con epilepsia). Poster presented at the 6 congreso latinoamericano de epilepsia (6th Latin American congress on epilepsy), Cartagena, Colombia, August 1 - 4.
van Iterson, L., & Augustijn, P. (2010). Cognitive outcome of childhood epilepsy in adolescence: utility of the Epilepsy Syndrome Severity Scale for Children (ESSS-C). Epilepsia, 51 (Suppl 4), 104.
van Iterson, L., & Augustijn, P. B. (2013). Rates of Reliable Cognitive Change (RC) after a change of WISC test version in children with refractory epilepsy: adjustment for Flynn effects. Poster presented at the 2013 Mid year meeting of the International Neuropsychological Society, Amsterdam, July 10 - 12.
van Iterson, L., & Augustijn, P. B. (2014). Cognitive patterns in children with epilepsy in relation to number of anti-epileptic drugs taken at time of neuropsychological testing. Perfil cognitivo en nin˜ os con epilepsia y el efecto de número de antiepilépticos durante la evaluación neuropsicológica, 8th LACE, Buenos Aires (Argentina), September 17 - 20.
van Iterson, L., San Miguel-Montes, L., & Rios, M. (2009). The factor analytic structure of the WISC-R and WISC-III in children with||epilepsy: a study across countries and cultures. Epilepsia, 50 (S10), 145.
van Iterson, L., San Miguel Montes, L., Rios-del Pozo, J., & Rios, M. (2009). Classificatory utility of large intra-individual WISC-R variability in focal vs generalizes seizures: Data across countries and cultures. Puerto Rico & The Netherlands.
Additional publications and posters (not related to the topics)
Augustijn, P. B., & van Iterson, L. (2011). Vitamin D status in an out clinic patient population of a tertiary referral epilepsy center. Epilepsia, 25 (suppl 6), 25.
van Iterson, L. (2002). Buidelnesten. Wandelingen door Colombia en Venezuela. Baarn: Zipatá.
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van Iterson, L. (2009). Cued learning trials for children with epilepsy in story retelling: gains at group level, but not all individuals thrive. Epilepsia, 50(4 (S)), 165 - 166.
van Iterson, L. (2010). Nidos de Oropéndola. A pie por los Andes de Colombia y Venezuela. Bogotá: La Serpiente Emplumada.
van Iterson, L., Augustijn, P., Mora, E., & Parra, P. (2005). Neuropsychological functioning during non convulsive status epilepticus in ring chromosome 20. Selective impact on cognitive measures. Epilepsia, 46, supplement 6, 3-415
van Iterson, L., Augustijn, P., & Neijens, L. (2008). Learning and forgetting of AVLT word lists in children with focal epilepsy. Journal of the International Neuropsychological Society, 14(2), 114.
van Iterson, L., & Broekhuis, J. B. C. (2007). De hellingshoek van het vergeten (The slope of forgetting). Epilepsie, 5, 15 - 16.
van Iterson, L., Davelaar, S., & Augustijn, P. B. (2014). Story retelling in 14 and 15-year old youngsters with epilepsy compared to control children matched for initial learning score: accelerated long term forgetting. Poster presented at the 11th European Congress of Epilepsy, Stockholm, June 29 – July 3.
Acknowledgements
ACKNOWLEDGEMENTS
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Acknowledgements
The children who participated in the testing are the most important in this work. Compacting
data renders individuals unrecognizable. Undertaking the testing, however, has been an effort
to identify each child’s individuality. Working with individual children and their parents has
infused my conviction, that the child tested today is the only one who counts. While
seemingly an iterative process, to engage in a new assessment is, in fact, a new adventure.
Beyond the most significant – the children – there are many I wish to mention and
thank in these pages. First, Professor Ley Sander, research director of Stichting Epilepsie
Instelligen Nederland, SEIN. Ley advised in search for promotores in an early stage,
commented critically on the work. He encouraged the thesis with words which got their
reassuring strength from their simplicity: “if you want to write a thesis, you can,” sometimes
adding, “the more, the merrier”. Obrigada, Ley, por sua confiança. By the same token, I wish
to thank SEIN and De Waterlelie , for the opportunity to work on this project.
Second, I am most indebted to Professor Peter F. de Jong at the University of
Amsterdam for his efforts to guide me as a clinician and external student. With his
background as a trained psychologist and methodologist, Peter imparted his overwhelmingly
razor-sharp thoughts in every discussion. He literally “promoted” the work to a new stage,
always improving it. It was humbling and instructive to experience how Peter’s own
professional and personal endeavours would express themselves on the thesis. Thank you for
making the project increasingly inspiring and challenging – hartelijk dank, Peter.
Third, this work owes much to Professor Alan S. Kaufman from Yale School of Medicine. It
does so in diverse ways, both related to his own work and as well as to him as a person and
supervisor. To start with his work: Alan Kaufman’s seminal work, Intelligent testing with the
WISC-R (Kaufman, 1979), provided the inspiration for the first building block of the present
work. I read his book in 1979 – 1980, when it had just appeared on the market, I had just
started my first job and the director of the institution on school advice gave me the book to
review and to advice whether it should be considered for the institution’s library.
Before detailing the other ways I am indebted to Professor Kaufman, I start with a
diversion which will lead to tell how I got to know him and his wife personally. This –
atypical – detour takes me to the Pacific coast of Colombia: In 2000, I spent some pleasant
days in a primitive hotel. The owner had been a famous local cook in the village in long
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forgotten years. When I knocked at her door, the clocks of the hotel had stopped ticking.
Time had moulded off the signs from the front, no longer identifying the house as a hotel or
restaurant. The lady received me – and later other guests as well – only hesitantly. She no
longer had the keys for the rooms. No wrinkle of surprise would show on her face when I told
her that other guests, returning drunk in the middle of the night, mistook my room for theirs.
With the devotion of a famous cook, my landlady sat down to accompany me at every
meal. While watching me as I ate, one evening she contended that she could have long been
dead. In the early sixties, she said, she had “given away her seat” to a school teacher who
urgently needed a place for a flight. She gave him the ticket she had booked for herself. As
she was no longer traveling, the morning of the flight she went to church. Meanwhile, the
teacher took her place in the airplane and left for the capital. At exiting the church, an
enormous turbulence startled my landlady. The airplane had crashed against the Andes. All
passengers died. “The teacher was replaced,” she added laconically (van Iterson, 2002, 2010,
unrelated publications).
So I could have been warned when, years later, I took my colleague’s place and went
to a conference on the Kaufman Adolescence and Adult Intelligence Test. I could have
known that taking someone’s seat could change your fortune. Fortunately, “being given
someone else’s seat” is not always for the worst – quite the contrary.
My colleague neuropsychologist from SEIN, Willem Alpherts, had registered for the
day conference on intelligence testing in Amsterdam but readily gave me his “seat” when he
noticed that I also was eager to go. Alan and Nadeen Kaufman gave a presentation on their
test which had just been published in German and Dutch, as well as a brilliant lecture on
stability and decline of cognitive abilities during the course of life – not unlike what we
describe in epilepsy at a much younger age.
After the conference, I approached Professor Kaufman with hesitant timidity. I was
thrilled to meet a man who was not exhausted after a daylong talk, by remarkably alert. “Send
your paper to me…” he said and scribbled his e-mail address on a piece of paper.
It was the beginning of intensive correspondence, joint research on the Dutch WISC –
it was the start of the dissertation. Alan provided examples of his own-work-in-progress to
help me get acquainted with the process of writing and editing. He asked me to comment on
them to obtain experience in reviewing. He guided the search of and dealing with journal
editors. Throughout the process of thinking and writing, Alan critically read every single
word. In Alan Kaufman I met a gentle person who swiftly would reply every letter, would
answer every question and would keep every promise. Together with Nadeen we shared an
ACKNOWLEDGEMENTS
209
evening with a private tour at the Louvre, concerts in Paris and in their home town San
Diego. Over a meal in a Japanese restaurant we discussed a thesis on the Wechsler Scales.
And yes, we shared the publication of what would be the first article. At my various visits, I
would leave San Diego with a suitcase full of books, the Spanish translation of his famous
seminal book being one of them.
Alan Kaufman did more. He gave me the case with the WISC-V test which was far
from being launched. He asked me to study it overnight, and let me comment on it the next
day. Nadeen and Alan, beyond your guidance, inspiration and scientific contribution, I want
to thank you for your friendship.
Fourth, I thank Bonne Zijlstra from the University of Amsterdam for sharing his expertise in
statistics and methodology which greatly improved the fourth and fifth paper. Also, I am
grateful for his reassuring words when most needed. I very much admired Bonne’s calm,
friendly and youthful wisdom.
I much appreciated Professor Aryan van der Leij’s enthusiasm when we first talked on the
idea of working on Wechsler scales in clinical samples. In every word I sensed the shared
clinical background and the shared interest of turning an “internal database” into an empirical
research project.
I wish to thank Professor Hilde Geurts and Professor Maurits van der Molen (University of
Amsterdam), Professor Wilma Resing (University of Leiden), Professor Pol Ghesquière
(Catholic University Leuven) and dr. Geert Thoonen (Radboud University Nijmegen) for
agreeing to take place in the “promotiecommissie”, for reading and rethinking the thesis.
To my co-author Paul Augustijn, child neurologist and epileptologist of SEIN, I am indebted
the insight in the classification of the epilepsies and the scoring of the syndrome severity, as
well as his solidarity throughout the years.
I thank Jacob Aten for his support and general indications about how the preparation
of a thesis and the finding of promoters take place and his keen interest during the course of
the project.
I thank Roos Rodenburg for her actual help in the process of finding the Dutch thesis
directors. She was the colleague from SEIN to connect me to the University of Amsterdam,
contending that is was worth analysing “een bak met data”, as she called the vast data
COGNITIVE PATTERNS IN PAEDIATRIC EPILEPSY
210
collection. Together with Roos, we extended the data with joint work with her students at the
University of Amsterdam. I thank her especially for her friendship. Roos Rodenburg and Paul
Augustijn, I am very pleased that both of you are willing to act as a paranymphs for the
ceremony.
I thank Ben Schomakers for his encouraging sentence: “you need not write a
proefboek, but if you want to write it, you can”, consistent over the years we shared. His
superb eloquence allowed him to configure a new word in order to make it sound an easy job.
I want to mention my colleagues at the various work places. Form earlier times, Han
Meliëzer and Dienke Evenhuis. More recently, through Willem Alpherts, Mieke Siffels,
Agnes Dommerholt, Erna Haverkort, Huibert Geesink and Dimitri Velis, I want to thank all
at SEIN’s psychology, neurology and neurophysiology departments. Through Lidwien
Neijens, Rien Moerland, Karin Luymes, Annemieke Smit, Marije van Wijngaarden, Jan van
der Kuip, Teus van den Brink, Piet Klein, José van Veen, Marian Fenenga, Eberdien
Karsdorp, Richard Donkers, Carola Bol, I want to thank all from De Waterlelie.
A Fernando Zamora le agradezco la revisión del Resumen.
It was Bernhard Sleumer, my brother-in-law, who long time ago, put forward: “why
not write a PhD thesis?” Sadly, he died the day after I sent the manuscript to the committee.
I also wish to say köszönöm to György Katalin, dankjewel to Marie and Mette
Lindhout, dankjewel to Bernhard and to my sister Foyita, and gracias to my brothers Victor
and Marnix for their explicit or tacit consent to spend my spare time at the computer, my
holidays at the library of Budapest, in hotel rooms of seldom visited Portuguese-speaking
islands, in down-town Cartagena or in a tiny study in Spain – instead of sharing my hours
with them.
Finally, I want to thank Dick for returning me my smile. Dick, it is simply wonderful to be
with the tender lovable person you are.
I dedicate this work to my mother for teaching her children that it is worthwhile to pursue
writing down their thoughts. I dedicate this work to the memory of my father for wordlessly
understanding that the instant a study is concluded, is the moment one has the least
understanding of the topic.
Baarn, Bogotá, Budapest, Buenos Aires, Cartagena, Cómpeta, Ilha do Mel, Leiden. May 2015.
About the author
ABOUT THE AUTHOR
213
About the author
Loretta van Iterson was born January 3rd 1956 in The Hague, The Netherlands, but was
raised in Bogotá, Colombia, where she moved with her family just after turning two years
of age. She had her kindergarten, primary and secondary
schooling at the Colegio Andino, the German school in Bogotá.
After completing her bachillerato, at age 17 she returned to The
Netherlands to study psychology at the Radboud University
(then called Katholieke Universiteit Nijmegen). By the time of
her graduation in developmental psychology in January 1980,
she was working as a child psychologist at the SABD, the school advice centre in Drenthe
that covered all schools for regular and special education in the province. This work gave
her the opportunity to get acquainted with all kinds of children with developmental needs.
Over time she changed her work premises to the school for children with psychiatric
disorders (Van der Reeschool, Smilde) and in 2002 she added to it the School De
Waterlelie (Cruquius) for children with epilepsy. From 2003, does the neuropsychological
assessment of children referred by the child neurologists in the clinics of Stichting
Epilepsy Instellingen Nederland, SEIN Heemstede and takes part of the Child Epilepsy
Centre (KEC).
In 1984, she did an informal neuropsychology internship with Barbara C. Wilson at North
Shore University Hospital in the state of New York. In 1992 – 1993, she specialized as a
child neuropsychologist at the European Graduate School of Child Neuropsychology
(EGSCN, PI Duivendrecht, Free University of Amsterdam). Being a native speaker of
Spanish and Dutch, in between, in 1987, she graduated as a sworn translator and
interpreter for the Dutch and Spanish languages in Utrecht.
Between 1979 and today, with refreshing changes in content over the years, most
of her career has been devoted to neuropsychological assessment of children and
youngsters with special needs, and the guiding of parents and teachers. The vast majority
of children are Dutch, but sometimes South American as well. Her field has been the
exploration of cognitive development in children and youngsters with mild intellectual
disabilities, specific learning disorders, psychiatric and behavioural disorders, and
children with epilepsy, in regular or special educational settings. Nowadays, together with
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214
the school for epilepsy, the tertiary clinic for children with epilepsy is her major focus of
activities. Besides her interest in intelligence testing, she has particular interest in
improving test norms. As such, with her students she has collected experimental norm
data for a variety of neuropsychological tests. She took the initiative for the design and
development of a Dutch test on story learning and retelling, intended especially for
children with epilepsy, and is currently working on the manual.
Between 2000 and 2003, she took part of the pilot study of the Indication
Committees for children with psychiatric disorders to aid in the development and try out
of diagnostic criteria, which later led to the “CvI cluster IV”. Between 2005 and 2013, she
was a member of the RSG, the registration committee for specialisms in the mental
health, an organ of the FGzP (now called Federatie gezondheidszorg psychologen en
psychotherapeuten). From 2007 onward, she has been a member of the editorial board of
“Epilepsie”, a Netherlands journal for professionals in the field of epilepsy. Since 2008
she participates in “EURAP extension protocol”-project on the development of children
born to mothers with epilepsy. Since 2014 she is a member of the board of the Dutch
chapter of the League against Epilepsy (LIGA).
Loretta van Iterson is registered as a specialist on Children and Youngsters
(specialist K&J, NIP), as a mental health psychologist (Gz, BIG), as well as specialist
Clinical Psychologist (KP, BIG, FGzP), and as a sworn translator Spanish (Utrecht).
She interrupted her work career twice to spend a sabbatical year in South America. The
first time, in the mid-1980s, she spent in Colombia, the second time, near the turning of
the century, in Venezuela and partly in Hungary.
She is a keen traveller, mostly to South America but to many other – preferably
remote – places as well. She wanders around these far places on her own, sometimes with
her partner, sometimes aiming at visiting her brothers and sister scattered around the
world. She has authored four Dutch books on travels in Latin America, one of which has
also been published in Spanish, in Bogotá in 2010.
As a partner to Dick, Loretta rejoices in the lovability, undertakings and accomplishments
of his children.